Forward Deployed Engineer - Business AI

Job Description This is a remote position. Manning Global, an Internationally renowned staffing and managed services organization specializing in providing staffing solutions for leading corporations in AI, IT & Telecoms, Engineering, Digital Media, Automotive and Renewable Energy is recruiting for Forward Deployed Engineer - Business AI on behalf of one of their blue-chip international clients to join their organization in the India. Job Title: Forward Deployed Engineer - Business AI Employment: Full time, Permanent Start Date: 09/2026 Country: India, Bengaluru - Remote Salary range: TBD Contact: Ashwini Chawan on: +49 (0) 89 23 88 98 35 Key Responsibilities We are seeking a Forward Deployed Engineer (FDE) to take technical ownership of Business AI Cloud deployments for enterprise clients. FDEs embed with the client, often inside the client's own environment and teams, and own the solution end to end: discovery, solution design, integration, agent development, production rollout, and optimisation after go-live. This is not a pure coding role and not a pure consulting role. It needs strong engineering, comfort with ambiguity, and the judgment to turn a loosely stated business problem into a working production system. Lead the technical design and implementation of large-scale agentic AI solutions on Business AI Cloud for enterprise clients. Own projects end to end: discovery and scoping, integration design, agent workflows, build, production rollout, and post-deployment optimisation. Act as the primary technical point of contact for the client throughout delivery and after go-live. Build production-grade components where configuration is not enough: custom integrations, APIs, tooling, and extensions to platform logic. Design and tune retrieval (RAG) pipelines, knowledge graphs, and fine-tuned small language models (SLMs) for domain accuracy. Define evaluation frameworks: success criteria, test sets, accuracy measurement, and regression checks that catch problems before the client does. Champion observability, monitoring, versioning, and telemetry so AI agents stay trustworthy and auditable. Turn field experience into reusable assets: frameworks, playbooks, and documentation that speed up the next deployment. Provide structured feedback on product gaps and client pain points to Client and to our internal teams. Experience/Qualifications: Bachelor's or master’s degree in computer science, data science, or a similar field, with 5+ years of engineering experience including 2+ years in customer-facing or field roles. Proven experience building and shipping production software: full-stack skills (Python plus Node.js or Go; React or a similar front-end framework), Docker, Kubernetes, and CI/CD. Strong enterprise integration experience: REST, SQL, GraphQL, webhooks, and data pipelines across enterprise systems. Hands-on production experience with LLM applications: prompt engineering, vector databases (for example Pinecone, Weaviate), RAG pipelines (for example LlamaIndex, Haystack), and agent orchestration frameworks (for example LangChain, LangGraph, CrewAI). MCP-based integration experience is a plus. Experience fine-tuning or distilling language models is a strong advantage. Ability to translate ambiguous client needs into clear engineering plans, and to communicate well with senior business and technical stakeholders. Willingness to work on-site with clients as required by the engagement.