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

Erforderliche Kompetenzen

Data platforms (data lakes, pipelines, transformations, data flow to models)Generative AI and Large Language Models (LLMs)Retrieval-Augmented Generation (RAG) and AI agentsReading and interpreting data models, architecture diagrams, and API specificationsCloud platforms and data/AI services (AWS and/or Azure)Familiarity with Snowflake, Databricks, or equivalent data platformsRapid prototyping using AI platforms and tools to demonstrate solutionsClient-facing presentation and senior stakeholder managementRunning discovery independently (requirements discovery and scoping)Writing Statements of Work (SOWs), project proposals, and technical specificationsDelivery ownership and accountability for outcomes in consulting/embedded delivery modelsManaging multiple workstreams concurrently in a fast-paced pod environmentMLOps, CI/CD, model registry, and model observabilityAPI design and enterprise integrationsSecurity, governance, and cost optimization for data and AI solutions

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