AI Tool Development : Design and build agentic AI platform components, including tools, customized agents, and workflows that automate business processes.
Generative AI & LLMs : Develop applications leveraging LLMs (OpenAI, Claude, LLaMA, Mistral) for code generation, text analysis, and content creation.
RAG & Knowledge Systems : Build and enhance Retrieval Augmented Generation (RAG) pipelines using vector databases (FAISS, PGVector, Pinecone).
AI-Augmented Development : Implement AI-assisted development workflows (e.g., using Cursor, LangChain, LangGraph) to accelerate coding, debugging, and testing.
Production Deployment (MLOps) : Deploy AI models into production environments using Docker, Kubernetes, and cloud AI services (AWS SageMaker, Azure ML, Vertex AI).
Performance Optimization : Implement evaluation pipelines to monitor agent quality, latency, cost, and reliability.
Required Technical Skills
Programming : Strong hands-on experience in Python (fastAPI/Flask).
AI Frameworks : Proficiency in LangChain, LangGraph, LlamaIndex, or Semantic Kernel.
Software Engineering : Knowledge of REST APIs, Git, CI/CD pipelines, and NoSQL/RDBMS databases (Preference, not necessary)
Machine Learning : Solid understanding of Transformer architectures, embeddings, and NLP techniques.
Cloud Platforms : In-depth experience with at least one major provider : AWS, Azure, or GCP.
Preferred Skills
Experience with LLM fine-tuning or customization methods.
Familiarity with Model Context Protocol (MCP) or tool-integration frameworks.
Qualifications
Bachelors or Masters degree in Computer Science, AI/ML, or a related technical field.
Proven track record of building and shipping production-grade AI systems (added advantage)
