Forward Deployed Engineer

Forward Deployed Engineer (FDE) – Agentic AI & Enterprise Solutions

Banking Experience is must.

Role Overview

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 adoption 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
• Optimize 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, Google Cloud Platform, 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