Forward Deployed Engineer - LLM/Gen AI

About this role

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

Originally posted on Himalayas