Oct 22, 2026
AI / ML
Remote
4 - 8 Years
Job Description
Data Scientist-Gen AI Consultant
Work timings - 1.00 - 10.00 P.M (Mon-Fri)
Contract duration - 3 months (extendable)
Experience - 4+ Years
4+ years in Data Science/AI-ML with proven, hands-on delivery of Generative AI and Agentic AI solutions in production environments
Mandatory: Hands-on expertise in Dataiku (including LLM Mesh) and Snowflake -must be able to speak to real implementation, not just familiarity.
Solid grounding in GenAI architecture - LLMs, prompt engineering, RAG, vector databases -and how these translate into enterprise-grade solutions.
Proven experience designing and building AI Agents, including writing detailed multi-page agent instructions, defining tools, and enabling autonomous decision-making.
Practical knowledge of Agent Tool Building and Multi-Agent Orchestration - architecting how multiple agents collaborate, hand off tasks, and execute complex workflows end-to-end.
Able to walk through a GenAI project end-to-end: problem statement, architecture, tech choices, trade-offs, and implementation outcomes - not just describe it at a high level.
Good to have: Pharma/Life Sciences domain exposure - enterprise data, research, or drug-discovery use cases.
Must-Have Stack:
Dataiku (LLM Mesh) + Snowflake + GenAI/Agentic AI implementation
Nice-to-Have: Pharma/Life Sciences domain experience
Work timings - 1.00 - 10.00 P.M (Mon-Fri)
Contract duration - 3 months (extendable)
Experience - 4+ Years
4+ years in Data Science/AI-ML with proven, hands-on delivery of Generative AI and Agentic AI solutions in production environments
Mandatory: Hands-on expertise in Dataiku (including LLM Mesh) and Snowflake -must be able to speak to real implementation, not just familiarity.
Solid grounding in GenAI architecture - LLMs, prompt engineering, RAG, vector databases -and how these translate into enterprise-grade solutions.
Proven experience designing and building AI Agents, including writing detailed multi-page agent instructions, defining tools, and enabling autonomous decision-making.
Practical knowledge of Agent Tool Building and Multi-Agent Orchestration - architecting how multiple agents collaborate, hand off tasks, and execute complex workflows end-to-end.
Able to walk through a GenAI project end-to-end: problem statement, architecture, tech choices, trade-offs, and implementation outcomes - not just describe it at a high level.
Good to have: Pharma/Life Sciences domain exposure - enterprise data, research, or drug-discovery use cases.
Must-Have Stack:
Dataiku (LLM Mesh) + Snowflake + GenAI/Agentic AI implementation
Nice-to-Have: Pharma/Life Sciences domain experience
Job Overview
Location
Remote – India
Job Type
Contract
Key Skills
Dataiku (LLM Mesh) + Snowflake + GenAI/Agentic AI implementation