GenAI Data Scientist

Years of Experience:

Responsibilities:

- Collaborate with product, engineering, and domain experts to identify high-impact GenAI opportunities and create actionable road maps.

- Design, build, and iterate on GenAI and Agentic AI solutions end-to-end, encompassing agent architecture and orchestration, tool and memory integration, goal-directed planning and execution, analytical model development, prompt engineering, robust testing, CI/CD, and full-stack integration.

- Process structured and unstructured data for LLM workflows by applying vectorization and embedding, intelligent chunking, RAG pipelines, generative SQL creation and validation, scalable connectors for databases and APIs, and rigorous data-quality checks.

- Validate and evaluate models, RAGs and agents through quantitative and qualitative metrics, perform error analysis, and drive continuous performance tuning.

- Containerize and deploy production workloads on Kubernetes and leading cloud platforms (Azure, AWS, GCP).

- Communicate findings and insights via dashboards, visualizations, technical reports, and executive-level presentations.

- Stay current with GenAI advancements and champion innovative practices across the organization.

Requirements:

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.

- 1-2 years of hands-on experience delivering GenAI solutions, complemented by 3-5 years of deploying machine learning solutions in production environments.

- Strong proficiency in Python including object-oriented programming (OOP), with additional experience in R or Scala valued.

- Proven experience with vector stores and search technologies (e.g., FAISS, pgvector, Azure AI Search, AWS OpenSearch).

- Experience with LLM-backed agent frameworks including LangChain, LangGraph, AutoGen, and CrewAI, as well as RAG patterns.

- Expertise in data preprocessing, feature engineering, and statistical experimentation.

- Competence with cloud services across Azure, AWS, or Google Cloud, including Kubernetes and Docker.

- Solid grasp of Git workflows, automated testing (unit, integration, end-to-end), and CI/CD pipelines.

- Proficiency in data visualization and storytelling for both technical and non-technical audiences.

- Strong problem-solving skills, collaborative mindset, and ability to thrive in a fast-paced environment.