Gen AI Engineer_5+ years

• 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

Benefits & 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.