20 May 2026

Data Platform Lead - US

Full Time

Multiple Locations

ROLE OVERVIEW We are looking for a Data Platform Lead to own the design, build, and governance of our enterprise data platform. This is a role within the Data COE, responsible for driving platform maturity across a federated, multi-cloud environment. You will set engineering standards, eliminate data duplication and drift across domains, and enable reliable, governed data access — without creating centralisation bottlenecks. KEY RESPONSIBILITIES Design and evolve the enterprise data platform spanning Snowflake and Databricks across Azure and AWS Build and maintain production-grade ELT/ETL pipelines using Spark, dbt, Airflow, and Kafka Define and enforce platform standards for ingestion, transformation, storage, and access across federated domain teams Drive data quality, data contract adoption, and lineage visibility across the estate Own cloud storage architecture across S3 and ADLS; lead CI/CD practices for all platform assets Lead containerized workload deployment using Kubernetes for data platform services Coordinate with domain teams to align on standards; represent platform engineering in architecture forums Mentor mid-level engineers and conduct technical design reviews for platform-impacting changes MUST-HAVE SKILLS Snowflake — expert-level: data modelling, performance tuning, role-based access, zero-copy sharing, dynamic data masking Databricks — production experience: Delta Lake, Unity Catalog, cluster management, Databricks Workflows Python and SQL — strong proficiency; Scala advantageous Apache Spark — distributed data processing at scale Apache Kafka — real-time event streaming, pipeline design, topic management Apache Airflow — pipeline orchestration, DAG design, dependency management dbt — transformation layer modelling, testing, documentation Cloud storage — S3 and ADLS; familiarity with Parquet, Delta, and partitioning strategies Kubernetes — container orchestration for data platform services CI/CD — GitHub Actions or equivalent for data platform assets (pipelines, schemas, infra) 8+ years of data engineering experience, with 3+ years in a lead or staff IC capacity GOOD-TO-HAVE SKILLS Data quality frameworks — Great Expectations or equivalent, embedded in pipeline execution Feature Store platforms — Feast, Tecton, or equivalent; ML data infrastructure design Data catalogue and lineage tooling — Amundsen, DataHub, or equivalent Data contracts as an engineering practice — schema registries, versioning, producer/consumer agreements Open table formats — Apache Iceberg; lakehouse architecture patterns Cross-cloud experience — Snowflake (Azure), Databricks, and BigQuery integrations WHAT WE'RE LOOKING FOR Systems thinker — able to see how pipeline, storage, and governance decisions interact at scale Able to influence without authority across autonomous, federated domain teams Opinionated on standards and quality, but pragmatic in how they are adopted across an existing estate Strong communicator — able to translate platform complexity to both engineering peers and business stakeholders Ownership-driven, with a track record of improving platform reliability, cost efficiency, and developer experience

15 May 2026

Senior QA Engineer

Full Time

Bangalore Karnataka

Role OverviewWe are looking for a Senior QA Engineer to lead validation of AI use cases and solution approaches, ensuring that proposed solutions are robust, reliable, and aligned with problem requirements before productionization. Key Responsibilities•Define evaluation frameworks for AI/LLM-based use cases•Design and execute advanced test scenarios for model behavior and edge cases•Validate solution approaches during prototyping and experimentation phases•Work closely with stakeholders to ensure solution correctness and feasibility•Establish metrics for evaluating model performance and output quality•Guide teams on best practices for AI validation and testing•Mentor junior QA engineers in analytical and evaluation approaches Required Skills•Strong understanding of AI/ML systems and LLM behavior•Experience in testing complex, non-deterministic systems•Ability to define evaluation frameworks and validation strategies•Strong analytical and problem structuring skills Good to Have•Experience with LLM evaluation tools and techniques•Exposure to prompt engineering and model tuning•Familiarity with Python and experimentation workflows What We’re Looking For•Strong judgment and critical thinking•Ability to validate “is this the right solution?” vs just “does it work?”•Comfort with ambiguity and iterative problem-solving

15 May 2026

Data Platform Software Engineer

Full Time

Bangalore Karnataka

Role OverviewWe are looking for a Data Platform Software Engineer to build and scale robust, high-performance data platforms that power analytics, AI/ML, and business decision-making. This role will focus on designing and optimizing data pipelines, ensuring data reliability, and enabling seamless data access across the organization. Key Responsibilities•Design, build, and maintain scalable data pipelines using modern data stack tools•Develop and optimize data models and workflows on platforms like Databricks and Snowflake•Ensure data quality, integrity, and availability across systems•Collaborate with engineering, analytics, and product teams to enable data-driven use cases•Improve performance and cost efficiency of data processing and storage•Implement best practices for data governance, security, and access control•Support real-time and batch data processing requirements Must-Have Skills•Strong experience with Databricks and/or Snowflake•Proficiency in SQL and Python (or Scala)•Hands-on experience building ETL/ELT pipelines•Understanding of data warehousing concepts and data modeling•Experience working with large-scale datasets and distributed systems•Familiarity with cloud platforms (AWS / Azure / GCP) Good-to-Have•Experience with streaming frameworks (Kafka, Spark Streaming)•Exposure to AI/ML data pipelines•Knowledge of data governance and security frameworks•Experience with orchestration tools like Airflow What We’re Looking For•Strong problem-solving mindset with a focus on performance and scalability•Ability to work in cross-functional teams and translate business needs into data solutions•Ownership-driven approach with attention to detail and quality

15 May 2026

Data Platform Software Engineer

Full Time

Bangalore Karnataka

Role Overview We are looking for a Data Platform Software Engineer to build and scale robust, high-performance data platforms that power analytics, AI/ML, and business decision-making. This role focuses on designing and optimizing data pipelines, ensuring data reliability, and enabling seamless data access across the organization. Key Responsibilities Design, build, and maintain scalable data pipelines using modern data stack tools Develop and optimize data models and workflows on platforms such as Databricks and Snowflake Ensure data quality, integrity, and availability across systems Collaborate with engineering, analytics, and product teams to enable data-driven use cases Improve performance and cost efficiency of data processing and storage Implement best practices for data governance, security, and access control Support both real-time and batch data processing requirements Must-Have Skills Strong experience with Databricks and/or Snowflake Proficiency in SQL and Python (or Scala) Hands-on experience building ETL/ELT pipelines Solid understanding of data warehousing concepts and data modeling Experience working with large-scale datasets and distributed systems Familiarity with cloud platforms (AWS / Azure / GCP) Good-to-Have Skills Experience with streaming frameworks such as Kafka or Spark Streaming Exposure to AI/ML data pipelines Knowledge of data governance and security frameworks Experience with orchestration tools like Airflow What We’re Looking For Strong problem-solving mindset with a focus on performance and scalability Ability to work in cross-functional teams and translate business needs into data solutions Ownership-driven approach with high attention to detail and quality

15 May 2026

Data Platform Software Engineer — Lead

Full Time

Bangalore Karnataka

ROLE OVERVIEW We are looking for a Data Platform Lead to own the design, build, and governance of our enterprise data platform. This is a role within the Data COE, responsible for driving platform maturity across a federated, multi-cloud environment. You will set engineering standards, eliminate data duplication and drift across domains, and enable reliable, governed data access — without creating centralisation bottlenecks. KEY RESPONSIBILITIES Design and evolve the enterprise data platform spanning Snowflake and Databricks across Azure and AWS Build and maintain production-grade ELT/ETL pipelines using Spark, dbt, Airflow, and Kafka Define and enforce platform standards for ingestion, transformation, storage, and access across federated domain teams Drive data quality, data contract adoption, and lineage visibility across the estate Own cloud storage architecture across S3 and ADLS; lead CI/CD practices for all platform assets Lead containerized workload deployment using Kubernetes for data platform services Coordinate with domain teams to align on standards; represent platform engineering in architecture forums Mentor mid-level engineers and conduct technical design reviews for platform-impacting changes MUST-HAVE SKILLS Snowflake — expert-level: data modelling, performance tuning, role-based access, zero-copy sharing, dynamic data masking Databricks — production experience: Delta Lake, Unity Catalog, cluster management, Databricks Workflows Python and SQL — strong proficiency; Scala advantageous Apache Spark — distributed data processing at scale Apache Kafka — real-time event streaming, pipeline design, topic management Apache Airflow — pipeline orchestration, DAG design, dependency management dbt — transformation layer modelling, testing, documentation Cloud storage — S3 and ADLS; familiarity with Parquet, Delta, and partitioning strategies Kubernetes — container orchestration for data platform services CI/CD — GitHub Actions or equivalent for data platform assets (pipelines, schemas, infra) 8+ years of data engineering experience, with 3+ years in a lead or staff IC capacity GOOD-TO-HAVE SKILLS Data quality frameworks — Great Expectations or equivalent, embedded in pipeline execution Feature Store platforms — Feast, Tecton, or equivalent; ML data infrastructure design Data catalogue and lineage tooling — Amundsen, DataHub, or equivalent Data contracts as an engineering practice — schema registries, versioning, producer/consumer agreements Open table formats — Apache Iceberg; lakehouse architecture patterns Cross-cloud experience — Snowflake (Azure), Databricks, and BigQuery integrations WHAT WE'RE LOOKING FOR Systems thinker — able to see how pipeline, storage, and governance decisions interact at scale Able to influence without authority across autonomous, federated domain teams Opinionated on standards and quality, but pragmatic in how they are adopted across an existing estate Strong communicator — able to translate platform complexity to both engineering peers and business stakeholders Ownership-driven, with a track record of improving platform reliability, cost efficiency, and developer experience

15 May 2026

Platform Data Governance Lead

Bangalore Karnataka

Role Overview We are looking for a Platform Data Governance Lead to own the design, implementation, and ongoing management of our enterprise data governance framework. You will define governance standards, enforce data quality practices, build and operate our data catalogue, and enable trusted, compliant data access across a federated, multi-cloud environment. Key Responsibilities Design and implement the enterprise data governance framework — policies, ownership models, data contracts, and classification standards across federated domain teams Define and operationalise data quality standards, SLAs, and monitoring workflows; embed quality frameworks (e.g. Great Expectations) into pipelines Own the enterprise data catalogue (e.g. DataHub, Amundsen) — deployment, metadata enrichment, lineage visibility, and self-service data discovery Partner with Legal, Compliance, and Risk to align governance with regulatory obligations (GDPR, PII, data residency) Influence domain teams to adopt governance as an enabler; represent the practice in architecture forums Mentor mid-level engineers and analysts on governance, quality, and catalogue standards Must-Have Skills Data governance frameworks — enterprise policy design, data ownership models, data contracts, and stewardship operating models Data quality — Great Expectations or equivalent; quality rule authoring, SLA definition, and pipeline-embedded checks Data cataloguing — DataHub, Amundsen, or equivalent; metadata management, lineage tracking, and business glossary curation SQL and Python — proficiency for data profiling, quality automation, and governance tooling Snowflake and Databricks / Unity Catalog — governance features: RBAC, masking, classification, lineage Regulatory awareness — GDPR, PII handling, data residency, and sensitivity classification 8+ years in data roles, with 3+ years in a governance, data quality, or senior IC capacity Good to Have Schema registries, open table formats (Apache Iceberg), dbt governance, data mesh operating model What We're Looking For Systems thinker who sees how governance, quality, and catalogue decisions interact across the full estate Influencer — able to embed governance as an enabler across autonomous teams without central authority Opinionated on standards, pragmatic on adoption; strong communicator with both technical and business audiences Ownership-driven — track record of improving data trust, reducing compliance risk, and building stewardship culture

15 May 2026

AI SECURITY & PRIVACY LEAD

Full Time

Bangalore Karnataka

ROLE SUMMARY We are looking for an AI Security & Privacy Lead to own the end-to-end security posture of our AI/ML systems—spanning Generative AI applications, Agentic AI workflows, RAG pipelines, and production ML infrastructure. You will embed security and privacy by design into every stage of the AI lifecycle, from model training and fine-tuning through deployment, integration, and autonomous agent execution. This role bridges deep AI/ML expertise with offensive security, data privacy engineering, and regulatory compliance to ensure our intelligent systems are trustworthy, resilient, and safe at scale. KEY RESPONSIBILITIES Define and execute the organisation’s AI security strategy covering threat modelling, risk assessment, and security architecture for GenAI, Agentic AI, RAG, and traditional ML systems. Lead LLM red-teaming and adversarial testing—design and run prompt injection attacks (direct, indirect, multi-turn), jailbreak assessments, data extraction probes, and model manipulation tests to identify vulnerabilities before production release. Architect and implement guardrails, input/output filtering, content moderation, hallucination detection, and toxicity screening pipelines to ensure safe and policy-compliant GenAI outputs. Secure Agentic AI systems by enforcing least-privilege tool access, sandboxed execution environments, action approval workflows, human-in-the-loop gates for high-risk operations, and agent behaviour boundary enforcement across multi-agent orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen). Design security controls for RAG pipelines—protect vector databases from poisoning attacks, enforce document-level access control in retrieval, prevent sensitive data leakage through embeddings, and validate retrieval-grounded outputs against source authority. Own AI data privacy engineering—implement PII detection and redaction, differential privacy, data anonymisation/pseudonymisation, consent management, and data minimisation practices across training datasets, fine-tuning corpora, and inference inputs/outputs. Drive compliance with GDPR, CCPA, EU AI Act, NIST AI RMF, ISO 42001, SOC 2, and industry-specific regulations (HIPAA, PCI-DSS) as they apply to AI/ML systems, ensuring audit readiness and documentation. Build AI security observability—deploy monitoring for anomalous model behaviour, adversarial input detection, data exfiltration attempts, agent action audit trails, and token-level cost anomaly alerts using SIEM integration and custom telemetry. Establish secure MLOps pipelines—model signing, provenance tracking, supply chain security for open-source models (SBOM for AI), secure model registries, encrypted model artefacts, and tamper-proof experiment tracking. Develop and deliver AI security training, threat awareness programmes, and secure-by-design guidelines for engineering, data science, and product teams across the organisation. Lead incident response for AI-specific security events—prompt injection breaches, model theft, training data poisoning, adversarial attacks in production, and agent autonomy failures. REQUIRED QUALIFICATIONS 8–13 years of combined experience in cybersecurity, AI/ML engineering, or security engineering, with 3+ years focused on AI/ML security. Bachelor’s/Master’s in Computer Science, Cybersecurity, AI/ML, or a related field. Deep understanding of LLM architectures, transformer internals, fine-tuning workflows, and GenAI application stacks—sufficient to identify and exploit security weaknesses. Hands-on experience with LLM red-teaming, prompt injection testing, jailbreak methodologies, and adversarial ML techniques (evasion, poisoning, model inversion, membership inference). Strong knowledge of AI privacy techniques: PII detection/redaction (Presidio, spaCy), differential privacy, federated learning, data anonymisation, and privacy-preserving ML. Proven experience securing agentic AI systems—tool-use access controls, agent sandboxing, action boundaries, and multi-agent trust frameworks. Familiarity with regulatory frameworks: GDPR, CCPA, EU AI Act, NIST AI RMF, ISO 42001, OWASP Top 10 for LLMs, and MITRE ATLAS. Proficient in Python, security tooling, and cloud security across AWS, Azure, or GCP. PREFERRED QUALIFICATIONS Experience building AI guardrail frameworks (NVIDIA NeMo Guardrails, Guardrails AI, LLM Guard, Rebuff) and content safety systems. Background in offensive security, penetration testing, or red-team operations (OSCP, OSCE, GPEN certifications a plus). Hands-on experience with AI governance platforms (Fiddler, Arthur AI, Credo AI, IBM OpenPages) and model explainability tools (SHAP, LIME, Captum). Experience with secure multi-tenant RAG architectures, vector DB access controls, and embedding-level data isolation. Publications, conference talks, or CTF contributions in AI/ML security; certifications such as CISSP, CCSP, or AI-specific security credentials. TECHNICAL STACK LLM Security Prompt injection testing, jailbreak frameworks, OWASP LLM Top 10, MITRE ATLAS, Garak, PyRIT Guardrails NVIDIA NeMo Guardrails, Guardrails AI, LLM Guard, Rebuff, Lakera Guard Privacy & PII Presidio, spaCy NER, differential privacy (OpenDP), anonymisation, consent engines Agentic Security LangChain/LangGraph security, agent sandboxing, tool ACLs, action approval gates RAG Security Vector DB access control, embedding isolation, retrieval validation, document-level ACLs Compliance GDPR, CCPA, EU AI Act, NIST AI RMF, ISO 42001, SOC 2, HIPAA, PCI-DSS AI Governance Fiddler, Arthur AI, Credo AI, SHAP, LIME, Captum, model cards, datasheets Cloud Security AWS (IAM, GuardDuty, Bedrock Guardrails), Azure (Defender, Content Safety), GCP (DLP, VPC-SC) MLOps Security Model signing, SBOM for AI, secure registries, encrypted artefacts, audit trails

15 May 2026

Senior Data Scientist

Bangalore Karnataka

ROLE SUMMARY We are looking for a Senior Data Scientist with deep expertise across the full spectrum of AI/ML — including time series forecasting, computer vision, NLP, and Generative AI — to join our team. You will own the end-to-end lifecycle of machine learning solutions: from problem framing and exploratory analysis through model development, fine-tuning, deployment, and ongoing monitoring. This is a hands-on, high-impact role for someone who is equally comfortable building production-grade deep learning pipelines and translating complex model outputs into clear business recommendations. You will work closely with engineering, product, and leadership to drive AI adoption across the organisation. KEY RESPONSIBILITIES Machine Learning & Predictive Modelling Design, develop, and deploy supervised and unsupervised ML models for classification, regression, clustering, ranking, and recommendation systems. Build end-to-end model training pipelines including feature engineering, hyperparameter tuning, cross-validation, and performance benchmarking. Conduct rigorous model evaluation, error analysis, and A/B testing; own model performance in production. Implement ensemble methods, gradient-boosted models (XGBoost, LightGBM, CatBoost), and deep learning architectures as appropriate. Time Series Analysis & Forecasting Design and build scalable time series forecasting systems for demand planning, financial modelling, capacity planning, and operational metrics. Apply classical statistical methods (ARIMA, SARIMA, Exponential Smoothing, Prophet) alongside deep learning approaches (LSTM, GRU, Temporal Fusion Transformers, N-BEATS, DeepAR). Develop anomaly and change-point detection pipelines for real-time data streams. Handle challenges common to production time series: missing data, irregular sampling, multi-seasonality, hierarchical reconciliation, and cold-start forecasting. Computer Vision Build and fine-tune convolutional and transformer-based vision models (ResNet, EfficientNet, Vision Transformers) for image classification, object detection, and segmentation tasks. Develop image processing and augmentation pipelines; work with labelling tools and annotation workflows. Apply transfer learning and domain adaptation techniques to achieve strong results with limited labelled data. Integrate vision models into production systems via APIs and edge-deployment where required. Generative AI & Large Language Models Architect and implement GenAI solutions using Large Language Models (GPT-4, Claude, Gemini, LLaMA, Mistral) for enterprise use cases including summarisation, extraction, content generation, and intelligent agents. Design and build Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, FAISS) and embedding models. Fine-tune foundation models (LoRA, QLoRA, full fine-tuning) on proprietary datasets; manage training runs, evaluate outputs, and mitigate hallucination and bias. Develop advanced prompt engineering strategies, evaluation frameworks, and guardrails for safe, reliable generative outputs. Build agentic workflows and tool-use patterns using orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel). Model Fine-Tuning & MLOps Fine-tune pre-trained models (language, vision, multimodal) for domain-specific tasks using parameter-efficient techniques (LoRA, adapters, prefix tuning) and full fine-tuning where appropriate. Manage experiment tracking, model versioning, and reproducibility using tools such as MLflow, Weights & Biases, or DVC. Build and maintain CI/CD pipelines for ML model deployment, monitoring, and automated retraining. Implement model observability: data drift detection, performance degradation alerts, and feedback loops. Data Strategy & Stakeholder Engagement Translate ambiguous business problems into well-defined analytical frameworks with measurable outcomes. Partner with Data Engineering to design scalable data pipelines, feature stores, and data quality checks. Present model results, insights, and recommendations to senior leadership through clear data storytelling and visualisation. Mentor junior data scientists and analysts; contribute to team best practices, code reviews, and knowledge sharing. REQUIRED QUALIFICATIONS 6–8+ years of professional experience in data science, machine learning, or applied AI. Master’s or PhD in Computer Science, Statistics, Mathematics, Physics, Engineering, or a related quantitative field (or equivalent industry experience). Strong proficiency in Python and the core ML stack: scikit-learn, TensorFlow / Keras, PyTorch, XGBoost, LightGBM. Proven track record in time series forecasting at scale — both classical and deep learning approaches. Hands-on experience with computer vision frameworks (OpenCV, torchvision, Detectron2, or equivalent). Demonstrated experience building applications with LLMs: prompt engineering, RAG, fine-tuning (LoRA/QLoRA), and evaluation. Solid grounding in statistical inference, experimental design, Bayesian methods, and causal reasoning. Experience with cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML) and containerised deployments (Docker, Kubernetes). Proficiency with SQL and large-scale data processing (Spark, Databricks, BigQuery, or equivalent). Familiarity with MLOps tooling: MLflow, Weights & Biases, Kubeflow, or similar. PREFERRED QUALIFICATIONS Experience with multimodal models and architectures (vision-language models, diffusion models). Familiarity with reinforcement learning, RLHF, or reward modelling techniques. Hands-on experience with speech/audio processing or signal processing pipelines. Published research, patents, or notable open-source contributions in ML/AI. Experience working in regulated industries (finance, healthcare, energy) with model governance and explainability requirements (SHAP, LIME, Integrated Gradients). Knowledge of responsible AI practices, fairness auditing, and emerging AI regulatory frameworks.

15 May 2026

Senior AI Engineer

Full Time

Bangalore Karnataka

Role OverviewWe are looking for a Senior AI Engineer to lead identification, structuring, and solutioning of AI/GenAI use cases, working closely with stakeholders to translate business problems into scalable AI solution approaches. Key Responsibilities•Lead problem discovery and use-case identification across client or internal contexts•Define solution approaches using AI/ML/LLMs, balancing feasibility, impact, and scalability•Build and guide prototypes / POCs to validate solution hypotheses•Design LLM-based applications, including prompt strategies, orchestration, and evaluation•Collaborate with stakeholders to refine requirements and align on solution direction•Work closely with delivery teams to ensure smooth transition from prototype to production•Contribute to playbooks, frameworks, and reusable solution patterns•Mentor junior engineers and drive structured thinking in problem-solvingRequired Skills•Strong experience in Python and ML/DL frameworks (PyTorch / TensorFlow)•Hands-on experience with LLM applications and APIs•Ability to translate ambiguous problems into structured solutions•Strong understanding of AI solution design and trade-offs Good to Have•Experience with LangChain or similar orchestration frameworks•Familiarity with MLflow or experimentation tracking tools•Exposure to Docker / Kubernetes (from a feasibility lens, not deep infra)•Awareness of CI/CD workflows What We’re Looking For•Strong problem structuring and solutioning ability•Ability to operate in ambiguous, exploratory environments•Comfort with stakeholder interaction and iterative refinement•Balance of technical depth and practical application thinking

15 May 2026

QA Engineer

Full Time

Bangalore Karnataka

Role OverviewWe are looking for a QA Engineer to support validation of AI use cases and prototypes, ensuring correctness, consistency, and reliability of outputs during experimentation and early-stage solutioning. Key Responsibilities•Validate AI/ML and LLM outputs against expected behaviors and use-case requirements•Design test scenarios for edge cases, prompt variations, and data inputs•Support evaluation of model performance and output quality•Work with engineers to identify inconsistencies and improve solution robustness•Contribute to defining evaluation metrics and validation approaches•Document findings to support solution refinement and decision-making Required Skills•Strong analytical and problem-solving ability•Basic understanding of AI/ML and LLM concepts•Ability to think through edge cases and scenarios•Familiarity with Python (basic level) Good to Have•Exposure to LLM evaluation techniques•Understanding of prompt engineering concepts•Familiarity with experimentation workflows What We’re Looking For•Strong curiosity and critical thinking•Ability to work with ambiguity and evolving requirements•Attention to output quality, not just system behavior