Design, build, and manage end-to-end AI pipelines, including data ingestion, transformation, validation, model training, inference, deployment, and knowledge retrieval workflows
Leverage AWS and Azure AI/ML services (e.g., Bedrock, SageMaker, Azure OpenAI, Azure ML, Document Intelligence, vector databases, etc.) to accelerate development and deployment
Implement observability, evaluation, and monitoring mechanisms to track model accuracy, drift, retrieval quality, grounding fidelity, reliability, and task-level reasoning
Optimize RAG systems for latency, scalability, cost efficiency, and response quality in production environments
Collaborate with software engineers, data scientists, solution architects, and business stakeholders to ensure seamless solution design and transition to production
Support both rapid experimentation (POCs, pilots) and robust, scalable deployments, depending on the maturity of each use case
Stay up to date with emerging trends in Generative AI, AI agents, orchestration frameworks, RAG evaluation techniques, and continuous improvement methodologies
4 to 6 years of hands-on experience in AI/ML development or intelligent application engineering
Strong proficiency in Python and familiarity with modern ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, OpenAI APIs)
Hands-on experience designing and deploying RAG-based systems, including embeddings, vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search), and retrieval optimization techniques
Experience building APIs or backend services using FastAPI, Flask, or equivalent frameworks
Exposure to agent frameworks (e.g., LangGraph, AutoGen) and multi-agent orchestration patterns
Experience with orchestration standards or tools such as MCP or agent routing protocols is a plus
Familiarity with cloud platforms (AWS, Azure) and containerization tools (Docker)
Hands-on experience with cloud-based ML platforms such as Azure Machine Learning, AWS SageMaker, or Google AI/ML services
Experience with MLOps pipelines, CI/CD for model delivery, RAG evaluation frameworks (e.g., RAGAS), or model monitoring is a plus
Knowledge of AI system evaluation, observability, prompt performance testing, and grounding validation is a plus
Ability to work on cross-functional teams, balance multiple projects, and communicate effectively with technical and non-technical audiences
Cloud certifications in AI/ML services (Azure, AWS, or Google Cloud) are a plus
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
