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
- Drive capability building in LLMs, RAG, and Agentic systems

Governance

- Ensure ethical AI usage and secure deployment practices
- Maintain documentation for audit, compliance, and scalability

Improvement

- Continuously optimize:
- Latency
- Cost per inference
- Model performance

- Build reusable AI components and frameworks

OKRs

Objective 1: Build Production-Ready GenAI Systems

- Deliver at least 2 end-to-end GenAI applications (RAG/Agentic AI)
- Achieve 90% response relevance and grounding accuracy




- Reduce hallucination rates via improved retrieval pipelines

Objective 2: Enable Local LLM Ecosystem

- Deploy at least 1 production-grade local LLM stack
- Achieve 30 50% cost reduction vs API-based LLM usage
- Optimize inference latency by 20%

Objective 3: Strengthen MLOps & Scalability

- Achieve 95% uptime for AI services
- Automate 80%+ of deployment pipelines
- Reduce model deployment time by 30%

KPIs

KPI

Metric

Target

GenAI application success

Business adoption

1 2 production deployments/year

RAG accuracy

Grounded responses %

>90%

LLM latency

Response time

Pipeline reliability

Uptime

>95%

Deployment efficiency

CI/CD usage

>80% automated

Technical Skills & Tools

GenAI & Agentic AI

- LangChain, LlamaIndex, LangGraph
- AutoGen, CrewAI, Semantic Kernel
- Prompt engineering frameworks

LLMs & Deployment

- LLaMA, Mistral, Falcon, Gemma, QWen
- vLLM, Ollama, Hugging Face
- Quantization tools (GGUF, bitsandbytes)

RAG & Retrieval

- FAISS, Chroma, Weaviate, Qdrant
- BM25, hybrid retrieval techniques
- Embedding models (BGE, OpenAI, Instructor)

MLOps & Infrastructure

- Kubernetes, Docker
- Kafka (streaming pipelines)
- Airflow (workflow orchestration)
- CI/CD: GitHub Actions, Jenkins

Programming & Data

- Python, SQL, Spark
- FastAPI, REST APIs
- Distributed systems and microservices

Monitoring & Observability

- Prometheus, Grafana
- MLflow, Weights & Biases

Qualifications & Experience

Requirement

Details

Education

Bachelor s/Master s in CS, AI, ML, or related

Experience

6-10 years

Relevant Experience

5+ years in ML/AI, 2+ years in GenAI/LLMs

Industry

Fintech, Banking, AI-first tech companies preferred

Certifications

Cloud (AWS/GCP/Azure), ML, GenAI preferred

Success Profile

- Builds scalable GenAI systems from scratch
- Expert in local LLM hosting and optimization
- Delivers production-ready RAG and Agentic AI solutions
- Implements robust MLOps pipelines
- Drives AI-led transformation across business functions

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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