Senior Engineer, AVP at Deutsche Bank — Pune - Margarpatta | MNCNaukri

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Senior Engineer, AVP at Deutsche Bank — Pune - Margarpatta | MNCNaukri

Develops and deploys source code, including infrastructure and application-related configurations for AI/ML, Agentic AI, and large-scale data processing components, in accordance with software requirement specifications.
Provides Development activities for Projects, ETL pipelines, and AI infrastructure components (i.e., Python, PySpark, GCP Vertex AI, Agentic frameworks, Machine Learning pipelines, and Infrastructure as a Code).
Designs and builds Intelligent AI Agents capable of planning, tool-use, multi-agent collaboration, and executing complex reasoning tasks.
Designs, develops, and maintains scalable Batch and Real-time ETL/ELT pipelines to ingest, clean, and transform massive structured and unstructured datasets.
Debugs, fixes, and provides support to the L3 support team for deployed AI/ML models, data pipelines, and applications.
Verifies developed source code, data transformation logic, and prompt configurations by peer reviews (4-eyes principle).
Contributes to quality assurance by writing and conducting unit, integration, and evaluation testing specifically designed for both deterministic ETL pipelines and non-deterministic AI outputs (e.g., LLM evaluations).
Ensures architectural changes (as defined by Enterprise/AI/Data Architects) are implemented.
Contributes to problem and root cause analysis for cognitive pipelines, data pipeline bottlenecks, and ML model drift or performance degradation.
Integrates software components following the integration strategy, MLOps, and DataOps guidelines.
Works on technical continuous improvements across AI/ML and data modules regularly (e.g., query performance tuning, PySpark job optimization, latency optimization, context window management, cost reduction).
Where applicable, develops CI/CD routines to deploy AI models, data pipelines, and agentic workflows to target environments.
Provides Release Deployments on non-Production Management controlled environments.
Supports creation of Software Product Training Materials, Software Product User Guides, and Software Product Deployment Instructions for AI and data systems.
Collaborates with colleagues participating in other stages of the Software Development Lifecycle (SDLC), MLOps, and Data Lifecycle.