Forward Deployed Engineer (FDE) - Agentic AI & Enterprise

Role :- Forward Deployed Engineer (FDE) - Agentic AI & Enterprise Solutions

Location :-  (NY/ NJ/ Delaware/ Colomb Plano/ Houston / Chicago) 5 Days onsite

Duration: Long Term Contract

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:

o            Prompt design and prompt chaining

o            Tool grounding and retrieval augmented generation (RAG)

o            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:

o            Cursor

o            GitHub Copilot

o            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:

o            AI generated code and test artifacts

o            Policy and control validation

o            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:

o            Security, privacy, and data handling policies

o            Model risk management and AI governance frameworks

o            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