Description

Key Responsibilities Lead the design, development, and implementation of advanced Machine Learning, Generative AI, and Agentic AI solutions across structured and unstructured datasets. Build scalable, automated data science products, machine learning models, and reusable analytics assets aligned with business and client objectives. Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends and actionable insights. Design and prototype Agentic AI workflows using LLMs, retrieval systems, APIs, tools, structured data, and business rules. Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing, escalation, and human-approval workflows. Design and optimize RAG solutions, including embeddings, metadata, reranking, semantic retrieval, citation quality, and knowledge freshness. Develop comprehensive AI/ML evaluation frameworks covering accuracy, performance, reliability, hallucination risk, latency, cost, safety, and consistency. Make architectural and analytical decisions covering model selection, methodology, solution design, technical standards, and AI implementation practices. Lead code reviews, mentor data scientists, and promote best practices in software engineering, responsible AI, model governance, and analytics delivery. Partner with engineering, platform, data, and business teams to deploy, monitor, troubleshoot, and maintain production AI solutions. Translate complex analytical results into clear reports, visualizations, and presentations for technical and non-technical stakeholders. Collaborate with clients and business stakeholders to define analytical approaches supporting strategic, operational, and clinical objectives. Evaluate emerging AI technologies and industry trends and recommend opportunities to enhance organizational AI capabilities. Required Qualifications 7+ years of relevant experience in Data Science, Machine Learning, AI Engineering, or a related field; 8–10 years preferred. Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Econometrics, or a related field preferred; advanced degree is a plus. Strong expertise in Data Science methodologies, statistical modeling, Machine Learning, Generative AI, Agentic AI, and NLP. Strong hands-on experience with Python and analytical programming; experience with R, SQL, SAS, or similar tools is beneficial. Strong understanding of LLMs, prompt engineering, RAG, embeddings, semantic search, and enterprise AI architectures. Experience with vector databases, knowledge graphs, metadata management, or enterprise search platforms. Experience deploying, monitoring, evaluating, and maintaining production AI/ML solutions. Experience working with cloud-based and distributed data platforms, including Databricks or similar technologies. Knowledge of Responsible AI, model governance, explainability, bias detection, validation, and ethical AI practices. Experience designing human-in-the-loop workflows for governance, risk management, and user oversight. Strong analytical, problem-solving, communication, and presentation skills. Demonstrated experience providing technical leadership and mentoring data scientists. Healthcare data experience involving clinical, claims, operational, or financial datasets is preferred. Essential Skills Data Science & Machine Learning Python Generative AI / LLMs / Agentic AI RAG / NLP / Vector Search Desirable Skills Databricks PyTorch / TensorFlow / Scikit-learn LangChain / LangGraph Vector Databases Semantic Search Knowledge Graphs Model Evaluation & MLOps Responsible AI / Model Governance Healthcare Analytics
 

Key Skills

Education

Bachelor's degree