Senior Data Engineer - AI & Data in Pune, India

Location Name: Pune Corporate Office - Mantri
The MLOps Engineer will own the end-to-end operationalisation of machine learning, large language model (LLM), and agentic AI workloads on the Bajaj Finance Enterprise Data Platform — a 5PB+ medallion lakehouse built on Azure Databricks and Unity Catalog. This role sits at the intersection of data engineering, model lifecycle management, and AI governance, ensuring that every model — from classical ML to RAG pipelines and autonomous agents — is reproducible, explainable, observable, and production-safe.
The incumbent will architect and implement the MLOps and LLMOps platform on Databricks, leveraging Agentbricks (Databricks Agent Framework), Databricks Apps, MLflow, Feature Store, Model Serving, and Mosaic AI — embedding rigorous CI/CD, drift monitoring, cost governance, and responsible-AI guardrails across the full lifecycle. This is a high-impact, high-visibility role critical to delivering Bajaj Finance's AI-first data strategy at scale across 120M+ customer interactions.
Databricks Apps & Self-Serve AI
Develop and deploy internal AI-powered applications using Databricks Apps — enabling business users to interact with ML models, RAG systems, and analytics agents through governed, self-serve interfaces.
Integrate Databricks Apps with Unity Catalog row/column-level security, ensuring data access controls are enforced transparently without requiring users to understand the underlying platform.
Build reusable application templates and deployment blueprints for common use cases (credit decisioning dashboards, collections intelligence tools, KYC automation) to accelerate delivery across business units.
Monitoring, Observability & Governance
Implement comprehensive model monitoring: data drift (population stability index, KS-statistic), concept drift, prediction drift, and feature distribution shifts — with automated alerts and retraining triggers via Databricks Workflows.
Build model performance dashboards in Databricks SQL / Power BI tracking accuracy, F1, AUC, RMSE, and business KPIs (approval rate, delinquency lift) across all production models.
Enforce Unity Catalog-based data and model lineage — every model must have traceable lineage from raw source data through features to predictions, satisfying RBI Model Risk Management guidelines and BCBS 239.
document model cards and maintain model risk registers in collaboration with Risk and Compliance teams.
Implement cost governance: cluster auto-scaling policies, spot-instance strategies, DBU budget alerts, and compute right-sizing recommendations to optimise the platform spend within approved budgets.
Partner with Data Scientists, AI Engineers, Data Engineers, and Business stakeholders to productionise models rapidly without sacrificing quality or compliance.
Establish and evangelise MLOps best practices, coding standards, and platform conventions through documentation, internal training sessions, and code reviews.
Contribute to the EDIL technical roadmap — evaluating emerging Databricks capabilities (Delta Live Tables, Lakeflow, Genie Spaces, AI/BI Dashboards) and proposing adoption plans with clear ROI justification.|Implement CI/CD pipelines for ML code (Databricks Asset Bundles / DABs, Azure DevOps, GitHub Actions) ensuring reproducible model builds, automated testing, and zero-downtime deployments.
Govern the full model lifecycle: versioning, lineage tracking via Unity Catalog, promotion workflows (Dev Staging Production), and model archival with audit trails.
Establish and maintain Feature Store — curated, reusable feature sets across credit risk, fraud, customer propensity, and collections models — ensuring data freshness, SLA adherence, and lineage traceability.
Operationalise Databricks Model Serving (serverless + provisioned endpoints) and Mosaic AI for scalable, low-latency inference across batch and online serving patterns.
LLMOps — Large Language Model Lifecycle
Design and implement LLMOps pipelines for RAG-based applications on Databricks: document ingestion chunking embedding generation vector indexing (Mosaic AI Vector Search / Unity Catalog Volumes) retrieval LLM serving.
Implement prompt versioning, prompt evaluation frameworks (MLflow LLM Evaluate, Mosaic AI Evaluation), and automated hallucination / faithfulness / relevance scoring using LLM-as-a-Judge patterns.
Manage LLM fine-tuning workflows: curate supervised fine-tuning datasets, run PEFT/LoRA jobs on Databricks GPU clusters, register and serve fine-tuned models via MLflow Model Registry.
Build token-cost monitoring, latency tracking, and model quality dashboards; implement automated rollback triggers when LLM quality KPIs degrade beyond defined thresholds.
Enforce LLM governance: input/output guardrails, PII redaction, jailbreak detection, and compliance logging aligned with RBI and DPDP Act requirements.
Agentbricks & Agentic AI Operationalisation
Deploy and operationalise autonomous AI agents using Databricks Agentbricks (Agent Framework) — including tool-calling agents, multi-agent orchestration, and human-in-the-loop review gates.
Implement agent observability: trace logging (MLflow Traces), latency profiling, tool-call auditing, and failure mode analysis for production agents such as FinOps Sentinel and Governed Analytics.
Manage agent state and memory persistence using Databricks-native storage (Delta Lake, Unity Catalog) and integrate with external databases (CosmosDB, Neo4j) as required by agent workflows.
Collaborate with AI Engineers on agent architecture decisions and ensure all agentic workloads meet latency SLAs, cost budgets, and safety standards.
Model promotion from Staging to Production for models below defined risk thresholds after successful evaluation.
Compute cluster configurations, auto-scaling policies, and spot-instance strategies for ML workloads.
Agent trace sampling rates, logging retention policies, and observability dashboard design.
Balancing velocity and rigour: delivering fast model deployments across 50+ source systems and 120M+ customer records while maintaining strict audit trails demanded by RBI Model Risk Management frameworks.
LLM non-determinism in production: managing hallucination risk, prompt sensitivity, and output variability in customer-facing AI applications where errors have direct financial and regulatory consequences.
Agentic AI safety: ensuring autonomous agents operating on live financial data remain within sanctioned boundaries — particularly for high-stakes decisions such as credit line adjustments, fraud flags, and collections prioritisation.
Scale and latency: serving real-time inference (sub-100ms) while maintaining model quality and managing compute costs within budget.
Cross-functional alignment: coordinating model deployment gates across Data Science, Risk, Compliance, IT Security, and Business teams — each with different timelines, priorities, and risk appetites.
Keeping pace with the Databricks roadmap: the platform evolves rapidly (Agentbricks, Mosaic AI, Lakeflow); Required Qualifications And Experience
~ https://jobeax.com/link/2DENLGgQM7whDhaE / B.E. / S. in Computer Science, Information Technology, Data Science, Electrical Engineering, or a related quantitative discipline.

Graduates from IITs, NITs, BITS Pilani, or other Tier-1 institutions preferred; exceptional candidates from other institutions with demonstrable production AI/ML experience will be considered.
Work Experience: 2–4 years of total experience in data/AI engineering, with a minimum of 1 years of hands-on MLOps or LLMOps experience in a production environment.
Demonstrated experience deploying and monitoring ML models in production at scale — not prototypes or PoCs alone; evidence of model lifecycle ownership from training through retirement.
Proven experience with Azure Databricks in an enterprise context — ideally within BFSI (banking, financial services, insurance), e-commerce, or a large-scale consumer data platform.
Experience with LLM-based applications in production (RAG pipelines, LLM serving, prompt evaluation) is a strong differentiator.
Exposure to agentic AI frameworks (LangGraph, Agentbricks, CrewAI) in production or advanced PoC settings is highly valued.
Track record of building scalable, observable, and cost-efficient ML infrastructure — demonstrated through measurable outcomes (latency, accuracy, cost, reliability improvements).
Experience working in regulated industries (BFSI preferred) with exposure to model validation, audit documentation, and compliance frameworks is an advantage.