Forward Deployed Engineer (FDE)

Client:Mphasis
Role:Forward Deployed Engineer (FDE) - Agentic AI & Enterprise Solutions
Banking Experience:Mandatory
Location:Onsite - TX / NJ / NY / FL / Ohio / DE / Chicago
C2C Rate:$70/hr
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.
Deploy complex enterprise system 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 including:
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 including:
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 including backend APIs and distributed systems.
Experience building production-grade cloud-native platforms/applications.
Proficiency in at least one modern programming language such as Python Java Go or similar.
Solid understanding of system design scalability and reliability.
Skilled in hyperscale platforms: AWS GCP Azure or OpenShift.
Agentic AI & AI Engineering
Hands-on experience with agentic AI frameworks and orchestration patterns such as n8n LangGraph Semantic Kernel CrewAI etc.
Experience working with LLMs / foundation models in enterprise settings including Claude Gemini or OpenAI.
Strong skills in prompt engineering context engineering and tool integration.
Understanding of RAG memory systems and knowledge grounding.
Experience with spec-driven development covering 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.
Strong understanding of complex enterprise system integrations.
Preferred Qualifications
Experience in regulated industries particularlybanking financial services healthcare etc.
Exposure to AI governance model risk and compliance frameworks.
Prior experience in customer-facing engineering roles such as FDE Solutions Engineer or 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.

Required Skills:

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