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

Multiple Locations Openings for Forward Deployed Engineer (FDE) - Agentic AI & Enterprise Solutions as Contract Job Opportunity

Strong knowledge of AWS, Bedrock and Java is mandatory , JPMC operates primarily on AWS and Java, this is a key requirement.
Banking domain experience is Mandatory.
Role :- Forward Deployed Engineer (FDE) - Agentic AI & Enterprise Solutions
Location :- JPMC location (NY/ NJ/ Delaware/ Colomb Plano/ Houston / Chicago/ Tampa / SFO/ Dallas) 2-3 Days onsite
Number of positions 5
H1B will work and need old LinkedIn Like from 2015-2017 and someone started there career as Java Developer .
10+ Years experience.

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 and 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