Position Responsibilities:
- Design, build, and deploy enterprise search and information retrieval systems across structured and unstructured data sources.
- Develop data agents and AI-powered workflows that can reason over enterprise knowledge, retrieve relevant context, and support downstream user actions.
- Build and maintain real-time and batch data pipelines that power search indexing, retrieval, ranking, and agent orchestration.
- Partner closely with data scientists, product owners, architects, and data engineers to deliver end-to-end AI products.
- Contribute to scalable ML/AI infrastructure using AWS-native services and MLOps best practices including CI/CD, monitoring, reproducibility, governance, and observability.
- Help evaluate and improve search relevance, retrieval quality, latency, reliability, and responsible AI guardrails in production environments.
- Flexible and adaptable to learning and understanding new technologies.
- Highly self-motivated and directed.
- Demonstrate a commitment to *** core values.
Experience and Qualifications:
- 6+ years of hands-on experience in Machine Learning, Applied AI, Information Retrieval, Enterprise Search, or MLOps, with a proven track record of building production-ready solutions.
- 3+ years of experience developing and deploying Generative AI applications, including LLM-based and agentic AI solutions using decoder-only language models.
- Strong expertise in NLP, NLU, semantic search, vector databases, retrieval-augmented generation (RAG), ranking algorithms, and production-scale LLM applications.
- Advanced programming skills in Python, along with experience in SQL, PySpark, REST APIs, and containerization technologies such as Docker.
- Hands-on experience with AI orchestration frameworks such as LangChain and LangGraph, and high-performance inference frameworks including vLLM, SGLang, TensorRT-LLM, or ONNX Runtime.
- Experience designing, building, and optimizing scalable data pipelines for both batch and real-time processing to support AI/ML workloads.
- Strong knowledge of AWS cloud services, including SageMaker, Lambda, ECS/EKS, Step Functions, Glue, and related cloud-native technologies.
- Solid understanding of MLOps best practices, including CI/CD, model deployment, monitoring, observability, governance, and reproducible ML pipelines.
- Excellent analytical, problem-solving, and collaboration skills with the ability to deliver scalable AI solutions in a fast-paced environment.
Education:
- Master's or Ph.D. in Computer Science, Machine Learning, Software Engineering, Artificial Intelligence, or a related technical discipline.
