Design and deploy end-to-end generative AI solutions — from data ingestion and RAG pipelines to model serving, agent orchestration, and prompt engineering — in production environments.

Build and optimize Python-based backend services using LangChain, LlamaIndex, and agent frameworks (e.g., AutoGen, CrewAI) for enterprise use cases.

Integrate LLMs (OpenAI, Anthropic, Mistral, Llama 3, etc.) with enterprise systems (CRM, ERP, Knowledge Bases) via API gateways and vector DBs (Pinecone, Weaviate, FAISS).

Collaborate with data scientists to fine-tune, evaluate, and deploy open-source LLMs on cloud platforms (AWS, Azure, GCP) using SageMaker, Vertex AI, or Azure ML.

Implement observability, cost monitoring, and guardrails (safety, hallucination detection, rate limiting) for GenAI deployments.

Mentor junior engineers, document architecture decisions, and enforce MLOps best practices (CI/CD,



model versioning, drift detection).

Skills & Qualifications Must-Have

Python

LangChain

LlamaIndex

OpenAI API

FAISS

Pinecone

Vector Databases

LLM Fine-tuning

RAG Architectures

Agent Frameworks

API Gateway Integration

Cloud Platforms (AWS/Azure/GCP)

Model Monitoring Tools

CI/CD for ML

Preferred

AutoGen

CrewAI

MLflow Perks & Culture Highlights Work on bleeding-edge GenAI projects for global clients across healthcare, finance, and logistics.

Flexible PTO, quarterly hackathons, and dedicated AI research time.

Opportunity to publish whitepapers, speak at AI conferences, and influence enterprise AI roadmaps.

Skills: llm,agentic ai,gen ai

📌 Gen AI Engineer_5+ years (Gurugram)
🏢 Zorba AI
📍 Gurugram