Key Responsibilities

• Design and implement end-to-end AI solutions using LLMs (e.g., GPT-based or open-source models)
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases
• Develop agentic AI systems capable of autonomous reasoning, planning, and task execution
• Architect and deliver goal-based AI platforms with full-stack capabilities (frontend, backend, APIs)
• Integrate AI models into scalable applications using microservices architecture
• Deploy, monitor, and scale AI workloads on AWS (e.g., EC2, S3, Lambda, SageMaker)
• Apply NLP techniques for text processing, semantic search, classification, and summarization
• Work with vector databases (e.g., Pinecone, FAISS, OpenSearch)
• Ensure model performance, reliability, and security in production environments
• Collaborate with cross-functional teams including product, data, and DevOps

Required Skills & Qualifications

• Strong experience with LLMs (Large Language Models) and prompt engineering
• Hands-on expertise in RAG (Retrieval-Augmented Generation) frameworks
• Solid understanding of Agentic AI / Autonomous Agents
• Proficiency in Natural Language Processing (NLP)
• Experience with Python and AI/ML frameworks (e.g., LangChain, LlamaIndex, Hugging Face)
• Strong experience in AWS cloud services (SageMaker, Lambda, ECS, API Gateway, etc.)
• Experience building full-stack AI applications (React/Node.js or similar stack)
• Knowledge of vector databases and embeddings
• Familiarity with REST APIs, Docker, Kubernetes
• Understanding of MLOps practices (CI/CD, model monitoring, versioning)