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 protected, 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.

📌 Forward Deployed Engineer - AI (Bengaluru)
🏢 Insaito Software
📍 Bengaluru