Forward Deployed Engineer - AI

Role Overview

The Forward Deployed Engineer is a hands-on, build-and-ship technical role that bridges deep engineering execution with product ownership. You will design, build, and deploy production AI-powered solutions that transform, owning the full path from requirements to a deployed, adopted system while also shaping what gets built through lightweight product ownership. This role is engineering-first: you spend most of your time architecting, coding, integrating, and deploying AI systems, and the remainder partnering with stakeholders to prioritize, validate, and drive adoption.

Key Responsibilities

AI Engineering & Solution Development

• Design, build, and deploy production Generative AI solutions end-to- end.
• Architect and implement on cloud infrastructure.
• Build backend services and APIs.
• Implement retrieval pipelines.
• Build proofs-of-concept and MVPs rapidly to validate feasibility and business value, then harden them into reliable production systems.
• Troubleshoot hands-on during implementation and deployment, and provide direct, technical problem resolution.

Deployment, Integration & Operations

• Lead end-to-end deployment of AI solutions into live environments.
• Configure, customize, and integrate AI agents with existing systems and workflows.
• Containerize and operate services and ensure reliability and scalability.
• Monitor solution performance and implement improvements based on usage data and feedback.

Governance, Quality & Evaluation

• Build evaluation harnesses, guardrails, and human-in-the-loop controls to keep solutions safe, accurate, and compliant.
• Ensure AI solutions comply with governance frameworks, security standards, and ethical guidelines.
• Maintain quality standards throughout the development and deployment lifecycle.
• Document technical specifications, user guides, and deployment processes.

Product Ownership & Stakeholder Engagement

• Partner with client teams to understand pain points, gather requirements, and identify automation opportunities.
• Shape and prioritize the product roadmap and backlog for AI solutions, aligning with business objectives and client needs.
• Define user stories, acceptance criteria, and success metrics in collaboration with stakeholders.
• Train and enable end-users, drive adoption, gather post-deployment feedback, and iterate.
• Communicate progress, challenges, and outcomes to leadership and cross-functional teams.

Required Qualifications

Technical

• 1013 years in software engineering, solution architecture, or technical lead roles, including recent hands-on production Generative AI / ML work.
• Strong programming skills in Python (JavaScript / Java); comfortable across the full stack.
• Demonstrated experience building and deploying LLM applications: RAG, AI agents, agentic / multi-agent workflows, prompt engineering, and/or fine-tuning.
• Hands-on experience with at least one major cloud platform.
• Solid grasp of APIs, system integration, microservices, and data workflows.
• Proven end-to-end ownership from requirements through architecture, build, deployment, and iteration.

Product & Domain

• Experience translating business requirements into technical solutions and identifying automation opportunities in complex workflows.
• Exposure to product practices: roadmap, backlog, user stories, acceptance criteria, success metrics and Agile delivery.
• Excellent communication skills, with the ability to engage technical and non-technical audiences; client-facing experience preferred.

Technical Toolbox

• LLM ecosystem: OpenAI / Azure OpenAI, Anthropic Claude, AWS Bedrock, Llama, Hugging Face, Azure AI Foundry
• Agent & orchestration frameworks: LangChain, LangGraph, MCP
• Retrieval & evaluation: Vector DBs (Pinecone/Chroma/Weaviate), RAG evaluation
• Backend: FastAPI / Flask, Docker, PostgreSQL / MongoDB, microservices
• AI-assisted SDLC: Claude Code, GitHub Copilot
• Familiarity with AI governance, evaluation, and human-in-the-loop design.