GenAI / AI Agent Developer

Build production-ready Generative AI applications using Large Language Models (LLMs) and AI agents. Implement RAG systems, prompt engineering, and multi-agent workflows for intelligent automation. Key Responsibilities

• Design and implement LLM-powered applications and AI agents
• Build RAG (Retrieval Augmented Generation) systems with vector databases
• Develop advanced prompt engineering strategies and templates
• Create multi-agent systems with tool integration and orchestration
• Implement document processing pipelines and knowledge base ingestion
• Optimize LLM inference for cost, latency, and quality
• Integrate LLMs with business workflows and APIs
• Evaluate LLM outputs and implement guardrails and safety measures Required Skills LLM & Generative AI :
• Deep understanding of Large Language Models (GPT, Claude, Llama)
• Prompt engineering techniques (zero-shot, few-shot, chain-of-thought)
• RAG architecture and implementation patterns
• Context management and token optimization
• Fine-tuning and parameter-efficient methods (LoRA, QLoRA)
• Understanding of transformer architecture and attention mechanisms AI Agents & Orchestration :
• Agent frameworks and autonomous systems
• Tool calling and function integration
• Multi-agent communication and coordination
• Planning, reasoning, and reflection patterns
• Memory management for conversational AI Vector Search & Embeddings :
• Embedding models and semantic search
• Vector database operations and optimization
• Similarity search and retrieval strategies
• Chunking strategies and document preprocessing Required Tech Stack LLM Frameworks & APIs :
• LLM Providers : OpenAI API (GPT-4, GPT-3.5), Anthropic (Claude), OpenRouter
• Frameworks : LangChain, LlamaIndex, LiteLLM, Haystack
• Agent Frameworks : AutoGPT, LangGraph, CrewAI etc
• Open Source LLMs : Llama 3, Mistral, Mixtral (via HuggingFace) etc Vector Databases & Search :
• Vector DBs : Pinecone, Weaviate, Chroma, Qdrant, Milvus (any of one)
• Embeddings : OpenAI Embeddings, Sentence Transformers, Cohere
• Search : Elasticsearch, OpenSearch Development Tools :
• Languages : Python (expert)
• Web Frameworks : FastAPI, Flask, Streamlit
• Document Processing : LangChain Document Loaders, Unstructured, PyPDF2
• NLP Libraries : spaCy, NLTK, Hugging Face Transformers MLOps & Deployment :
• Model Serving : vLLM, Ray Serve, TGI (Text Generation Inference)
• Monitoring : LangSmith, Weights & Biases
• Containerization : Docker, Kubernetes
• Version Control : Git Cloud & Infrastructure :
• Cloud Providers : AWS (Bedrock, Lambda), Azure (OpenAI Service), GCP
• APIs : REST, WebSocket, GraphQL
• Caching : Redis Preferred Qualifications
• Bachelor's / Master's in Computer Science, AI, NLP, or related field
• Experience fine-tuning LLMs (LoRA, full fine-tuning)
• Knowledge of LLM evaluation frameworks (ROUGE, BLEU, BERTScore)
• Contributions to LLM / GenAI open-source projects
• Experience with multi-modal models (vision, audio) What Success Looks Like
• Production GenAI applications handling real user traffic
• High-quality LLM outputs with low hallucination rates
• Cost-optimized inference with acceptable latency
• RAG systems providing accurate, relevant context
• Robust agent systems completing complex multi-step tasks
• Well-structured prompts and reusable templates