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Job Description
Structured overview of role & requirementsAbout This Role
Develop and deploy cutting-edge AI models and solutions including traditional ML and Generative AI techniques, focusing on prompt engineering, fine-tuning, embeddings, and RAG architectures.
Build intelligent, autonomous or interactive agentic systems using frameworks like LangChain, LangGraph, AutoGen, and design end-to-end RAG pipelines covering document ingestion to contextual response generation.
Design modular APIs and manage full AI pipelines involving data handling, model training, deployment, and maintain observability for model performance and reliability.
Minimum Requirements
4 to 6 years of hands-on experience in AI/ML development or intelligent application engineering.
Proficiency in Python and experience with ML/AI libraries such as scikit-learn, PyTorch, TensorFlow, OpenAI APIs.
Experience with RAG systems including embeddings, vector databases (FAISS, Pinecone, Chroma, Azure AI Search), and API/backend development using FastAPI or Flask.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
Ideal Candidate Profile
Experienced in building and optimizing production-ready RAG systems on cloud platforms AWS and Azure, with familiarity in containerization (Docker) and cloud ML services (Azure ML, SageMaker).
Skilled in integrating multi-agent orchestration frameworks and advanced retrieval optimization techniques to enhance performance and scalability.
Capable of balancing rapid experimentation with scalable deployments across cross-functional teams, communicating effectively to both technical and non-technical stakeholders.