Manager - GenAI & ML Ops

Manager Generative AI / Agentic AI & MLOps Job Identification Field Details Job Title Manager Generative AI / Agentic AI Function / Department AI / Data Science Business Unit Digital / NBFC / Fintech Location Gurugram (Hybrid) Grade / Level / Band I1 Reports To SVP Data Science / AI Role Type Hybrid Employment Type Full-time Role Purpose This role is responsible for designing, building, and deploying end-to-end Generative AI and Agentic AI systems, with a strong focus on local LLM deployment, secure enterprise GenAI applications, and scalable MLOps pipelines. The position requires expertise in: Local RAG (Retrieval-Augmented Generation) pipelines Agentic AI frameworks (multi-step reasoning systems) Private LLM hosting and optimization Robust MLOps architecture (Kafka, Kubernetes, Airflow, CI/CD) Success in this role means delivering production-grade GenAI systems that are scalable, secure, cost-efficient, and deliver measurable business impact. Key Result Areas & Responsibilities 1. GenAI & Agentic AI Development (35%) Design and build end-to-end GenAI applications from scratch Develop Agentic AI systems using frameworks like: LangChain, LlamaIndex, LangGraph AutoGen, CrewAI, Semantic Kernel Implement multi-agent workflows for decision-making and automation Optimize prompt engineering, memory management, tool usage, and reasoning flows Outcome: Production-grade GenAI and Agentic AI applications delivering automation and intelligence 2. Local LLM Deployment & Optimization (20%) Deploy and manage local/private LLMs (on-prem or VPC environments) Work with models such as: LLaMA, Mistral, Mixtral, Falcon, Gemma, QWen Use inference frameworks: vLLM, Ollama, Hugging Face Transformers, TensorRT-LLM Optimize for: Latency, throughput, and cost Quantization and model compression (GGUF, INT4/8) Outcome: Secure, low-latency, cost-efficient LLM deployments 3. RAG Pipelines & Knowledge Systems (20%) Design and implement advanced RAG pipelines: Document ingestion, chunking, embedding, retrieval, re-ranking, Grounding Build local knowledge bases using: Vector DBs: FAISS, Chroma, Weaviate, Pinecone (optional hybrid), Qdrant Implement: Hybrid search (BM25 + vector) Context window optimization Develop domain-specific assistants and copilots Outcome: Accurate, grounded GenAI systems with high retrieval precision 4. MLOps, Data Engineering & Deployment (15%) Develop scalable MLOps pipelines using: Kubernetes (container orchestration) Kafka (real-time streaming) Airflow (workflow orchestration) Implement CI/CD pipelines for ML: GitHub Actions, Jenkins, GitLab CI Enable: Model versioning, monitoring, and rollback Logging, observability (Prometheus, Grafana) Outcome: Reliable, scalable, and automated AI deployment pipelines 5. Governance, Security & Responsible AI (10%) Implement guardrails for GenAI: Prompt injection protection Output validation and filtering Ensure compliance with: Data privacy policies AI governance frameworks Manage model risk and audit readiness Outcome: Secure and compliant AI systems ready for enterprise deployment Detailed Responsibilities Planning Define roadmap for GenAI, Agentic AI, and LLM adoption Identify use cases for automation, decisioning, and productivity gains Plan infrastructure for local AI deployment Operational Build and deploy GenAI applications end-to-end Maintain pipelines for training, inference, and evaluation Ensure high uptime and system reliability People Mentor team on GenAI frameworks and MLOps practices Manager Generative AI / Agentic AI & MLOps Job Identification Field Details Job Title Manager Generative AI / Agentic AI Function / Department AI / Data Science Business Unit Digital / NBFC / Fintech Location Gurugram (Hybrid) Grade / Level / Band I1 Reports To SVP Data Science / AI Role Type Hybrid Employment Type Full-time Role Purpose This role is responsible for designing, building, and deploying end-to-end Generative AI and Agentic AI systems, with a strong focus on local LLM deployment, secure enterprise GenAI applications, and scalable MLOps pipelines. The position requires expertise in: Local RAG (Retrieval-Augmented Generation) pipelines Agentic AI frameworks (multi-step reasoning systems) Private LLM hosting and optimization Robust MLOps architecture (Kafka, Kubernetes, Airflow, CI/CD) Success in this role means delivering production-grade GenAI systems that are scalable, secure, cost-efficient, and deliver measurable business impact. Key Result Areas & Responsibilities 1. GenAI & Agentic AI Development (35%) Design and build end-to-end GenAI applications from scratch Develop Agentic AI systems using frameworks like: LangChain, LlamaIndex, LangGraph AutoGen, CrewAI, Semantic Kernel Implement multi-agent workflows for decision-making and automation Optimize prompt engineering, memory management, tool usage, and reasoning flows Outcome: Production-grade GenAI and Agentic AI applications delivering a