Forward Deployed Engineer - LLM/Gen AI

 Posted an hour ago
  
 India
  
5-10 years experience
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AI Summary

The role involves leading discovery and delivering AI/data products by scoping deliverables and reviewing technical outputs. You will be responsible for owning delivery outcomes and presenting solutions to senior business and technology stakeholders.

Job Role: Forward Deployed Engineer - LLM/Gen AI

Experience: 5-10yrs

Location: Bengaluru (Remote)

Notice Period: Immediate or max 15days preferred

Technical Skills Required 

 
Solid understanding of data platforms — data lakes, pipelines, transformation, and how data flows from source to AI model 
Working knowledge of Generative AI, LLMs, and RAG pipelines — enough to scope deliverables and review outputs critically 
Ability to read and understand data models, architecture diagrams, and API specs — you don't write production code but understand what's being built 
Cloud platforms — AWS and/or Azure — data and AI services at a conceptual and delivery level 
Familiarity with Snowflake, Databricks, or equivalent — enough for informed conversations with data engineers 
Experience using AI platforms and tools to rapidly prototype and demonstrate solutions to clients 

Delivery & Client Skills Required 

 
6–10 years in AI/data product delivery, technical consulting, or engineering — not pure product management 
Proven ability to run discovery independently — without a senior consultant holding your hand 
Strong client-facing presence — comfortable presenting to senior business and technology stakeholders 
Experience writing SOWs, project proposals, or technical specifications that get signed off by clients 
Track record of owning delivery outcomes — accountable for the result, not just managing tasks 
Experience in consulting, professional services, or embedded delivery model strongly preferred 
Ability to manage multiple workstreams simultaneously in a fast-paced pod environment 



Requirements


Key Skills
• Cloud Architecture (AWS/Azure/GCP)
• AI/ML, Generative AI, and Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG) and AI Agents
• API Design and Enterprise Integrations
• Data Pipelines, Data Lakehouse, and Data Mesh
• MLOps, CI/CD, Model Registry, and Model Observability
• Security, Governance, and Cost Optimization


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