Are you a Senior Data Scientist who bridges the gap between traditional Machine Learning and cutting-edge Generative AI?
I am currently partnering with an enterprise product team to recruit a Senior Data Scientist (GenAI / RAG) to design, deploy, and scale production-grade AI systems.
This is not a pure research or proof-of-concept role. We need an experienced practitioner who has shipped production GenAI architectures, built agentic workflows, and can confidently consult with senior client stakeholders to articulate technical trade-offs.
Key Responsibilities
- Build & Deploy: Develop and put into production ML models, advanced RAG architectures, and autonomous agentic workflows to solve complex enterprise challenges.
- Cross-Functional Collaboration: Partner directly with Product Managers, Software Engineers, and Data Engineers to integrate AI solutions into flagship enterprise platforms.
- Stakeholder Engagement: Act as a technical consultant, explaining architectural decisions, trade-offs, and data-driven insights to both technical teams and non-technical client stakeholders.
- Mentorship: Provide guidance to junior team members and cultivate continuous learning within the engineering organization.
Technical Requirements
- Core ML / DS Foundation: 5+ years of experience in traditional ML, including classification, regression, forecasting, anomaly detection, feature engineering, and model evaluation.
- Production GenAI Experience: At least 12 months of continuous, recent hands-on production experience with LLMs (beyond hackathons, certifications, or academic projects).
- Agentic Workflows: Proven production experience with tools such as LangChain, LangGraph, Model Context Protocol (MCP), tool calling, and agent orchestration.
- RAG Pipeline Depth: Expertise in vector databases, hybrid retrieval, reranking mechanisms, and knowledge graphs.
- Cloud & Stack: Solid experience with AWS Bedrock (Azure OpenAI or Google Vertex AI also considered), Python, SQL, and big data platforms like Spark, Databricks, or Snowflake.
- Communication: Outstanding client-facing skills with the ability to lead architectural discussions confidently.
Nice-to-Haves
- Domain exposure in Energy, Utilities, or Oil & Gas.
- Familiarity with XGBoost, CatBoost, LLMOps, and GenAI evaluation or observability tooling.
If you are looking to take ownership of end-to-end AI products and drive high-impact implementation, please submit your CV.
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