Ai Engineer / Forward Deployed Engineer (FDE)

Forward Deployed Engineer (FDE) – Agentic Ai & Enterprise Solutions The Forward Deployed Engineer (FDE) is a hands-on, customer-facing engineering leader who bridges business intent and production-grade AI solutions. The FDE works directly with business stakeholders, product owners, and platform teams to translate ideas into deployable solutions using an enterprise-enabled agentic AI platform and a governed adEligible to work framework. This role blends solution engineering, AI engineering, and delivery leadership, with strong ownership from problem discovery → architecture → build → deployment → optimization. The FDE operates close to customers and internal product teams, ensuring solutions deliver measurable business outcomes while meeting enterprise, security, and compliance standards. Key Responsibilities • 1. Idea-to-Production Solution Delivery • Partner with business stakeholders to understand problem statements, workflows, and desired outcomes • Convert business intent into AI-enabled solution designs, agent workflows, and system architectures • Own end-to-end delivery: prototype → MVP → production rollout • Deploying enterprise complex systems integrations while solving critical business problems • Drive rapid iteration while maintaining enterprise-grade quality, security, and reliability • 2. Agentic AI Solution Engineering • Design and implement agentic workflows using enterprise agentic AI platforms • Orchestrate multi-agent systems that handle reasoning, planning, execution, validation, and monitoring • Encode business logic, SOPs, policies, and controls into autonomous or semi-autonomous agents • Apply human-in-the-loop, guardrails, and fallback mechanisms where required • 3. Frontier Models & Context Engineering • Work with frontier foundation models (LLMs, multimodal models) and enterprise-approved model stacks • Perform context engineering: prompt design and prompt chaining, tool grounding and retrieval-augmented generation (RAG), knowledge graph and memory integration • Eligible to workimize solutions for accuracy, latency, cost, and reliability • 4. AI-First Engineering & Developer Tooling • Leverage AI-assisted development tools to accelerate delivery: Cursor, GitHub Copilot, AI-assisted testing, code review, and refactoring tools • Establish AI-augmented engineering workflows across design, build, test, and release phases • Coach teams on effective human-AI collaboration in engineering • 5. Intelligent CI/CD & MLOps • Design and implement intelligent CI/CD pipelines integrating: AI-generated code and test artifacts, policy and control validation, automated security and compliance checks • Integrate agentic workflows into DevSecOps / MLOps pipelines • Ensure repeatable, auditable, and scalable deployments across environments • 6. Enterprise Readiness & Governance Alignment • Ensure solutions comply with: security, privacy, and data-handling policies, model risk management and AI governance frameworks, regulatory and audit requirements (especially in regulated industries) • Collaborate with platform, security, and governance teams to operationalize guardrails • Contribute patterns, blueprints, and reusable assets to the enterprise AI platform • 7. Customer & Stakeholder Engagement • Act as a trusted technical advisor to customers and internal stakeholders • Present architectures, demos, and outcomes to engineering leaders, business heads, and executives • Gather feedback from production usage and continuously improve solutions • Serve as the "voice of the customer" back into platform and product teams Required Skills & Experience • Core Engineering & Architecture • Strong background in software engineering (backend, APIs, distributed systems) • Experience building production-grade cloud-native platforms/applications • Proficiency in at least one modern programming language (Python, Java, Go, or similar) • Solid understanding of system design, scalability, and reliability • Skilled in hyperscale platforms (AWS, Eligible to workP, Azure, OpenShift) • Agentic AI & AI Engineering • Hands-on experience with agentic AI frameworks and orchestration patterns (n8n, LangGraph, Semantic Kernel, CrewAI, etc.) • Experience working with LLMs / foundation models in enterprise settings (Claude, Gemini, OpenAI) • Strong skills in prompt engineering, context engineering, and tool integration • Understanding of RAG, memory systems, and knowledge grounding • Spec driven development – Architecture, Security and Application frameworks. • AI Tooling & Productivity • Practical experience using Cursor, GitHub Copilot, or similar AI coding tools • Familiarity with AI-assisted testing, documentation, and code review • Ability to design AI-first developer workflows • DevOps, CI/CD & Platform Integration • Experience with CI/CD pipelines, infrastructure as code, and cloud platforms • Understanding of DevSecOps and automated control enforcement • Familiarity with MLOps concepts for model lifecycle and monitoring • Enterprise & Soft Skills • Strong problem-solving and analytical mindset • Ability to work in ambiguous, fast-moving environments • Excellent communication skills with both technical and non-technical stakeholders • Customer-centric mindset with ownership and accountability • Good handle on complex enterprise system integrations • Preferred Qualifications • Experience in regulated industries (banking, financial services, healthcare, etc.) • Exposure to AI governance, model risk, and compliance frameworks • Prior experience in customer-facing engineering roles (FDE, Solutions Engineer, Field Engineer) • Experience contributing to platform blueprints, accelerators, or internal frameworks What Success Looks Like • Business ideas move to production faster and with higher confidence • Agentic AI solutions deliver measurable business outcomes • Engineering teams adopt AI-first workflows with strong governance • Customers trust the platform and the FDE as a strategic delivery partner