Description

• 7+ years of relevant experience in data science, analytics, machine learning, or AI.
• Expertise in statistical modeling, machine learning, generative AI, agentic AI, and natural language processing.
• Strong knowledge of LLMs, prompt engineering, RAG, autonomous decision-making frameworks, and human- in-the-loop workflows.
• Programming expertise in Python, R, SQL, SAS, or similar analytical tools, with cloud-based or distributed platforms such as Databricks.
• Experience with vector databases, semantic search, knowledge graphs, metadata management, enterprise search, and production AI deployment and monitoring.

Roles & Responsibilities
Design and implement advanced machine learning, generative AI, and agentic AI solutions across structured and unstructured data.
· Build and deploy scalable, automated data science products and reusable analytical assets.
· Design agentic AI architectures using LLMs, retrieval systems, structured data, APIs, tools, business rules, routing, and approval checkpoints.
· Optimize RAG solutions and develop evaluation frameworks covering accuracy, reliability, hallucination risk, latency, cost, safety, and consistency.
· Partner with engineering, platform, business, stakeholders, and clients to deploy, monitor, maintain, and communicate production-ready AI solutions.

Generic Managerial Skills, If any
• Lead complex analytics initiatives and provide technical guidance.
• Mentor data scientists and lead code reviews.
• Make analytical and architectural decisions and establish technical standards.
• Communicate complex analytical findings to technical and non-technical audiences.
• Collaborate with stakeholders and clients across strategic, operational, and clinical objectives.

Key Words to search in Resume
• AI Engineer; Data Scientist; machine learning; statistical modeling; natural language processing.
• Generative AI; Agentic AI; LLM; prompt engineering; RAG; autonomous decision-making; human-in-the-loop.
• Python; R; SQL; SAS; Databricks; distributed computing; cloud data platforms.
• Vector database; semantic search; knowledge graph; metadata management; enterprise search.
• Responsible AI; model governance; explainability; bias detection; hallucination evaluation; production AI monitoring

Key Skills

Education

Bachelor's degree