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Forward Deployed Engineer - LLM/Gen AI

Summary

Forward Deployed Engineer driving LLM/Gen AI client delivery — scoping solutions, prototyping with AI tools, and owning outcomes across data platforms, RAG pipelines, and cloud services (AWS/Azure) in a consulting model.

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

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

See also

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