01Overview
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
Advantages & Culture Highlights
Work on bleeding-edge GenAI projects for global clients across healthcare, finance, and logistics.
Adaptable PTO, quarterly hackathons, and dedicated AI research time.
Chance to publish whitepapers, speak at AI conferences, and influence enterprise AI roadmaps. .
