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)

AI Engineer