Forward Deployed Engineer -Agentic AI and Enterprise Solutions

Job Title

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 bridgesbusiness intent and production‐grade AI solutions. The FDE works directly with business stakeholders, product owners, and platform teams totranslate ideas into deployable solutions using anenterprise‐enabled agentic AI platform and a governed adoption framework.

This role blendssolution engineering, AI engineering, and delivery leadership, with strong ownership fromproblem discovery → architecture → build → deployment → optimization. The FDE operates close to customers and internal product teams, ensuring solutions delivermeasurable business outcomes while meetingenterprise, security, and compliance standards.

Key Responsibilities

1. Idea‐to‐Production Solution Delivery

• Partner with business stakeholders tounderstand problem statements, workflows, and desired outcomes
• Convert business intent intoAI‐enabled solution designs, agent workflows, and system architectures
• Own end‐to‐end delivery:prototype → MVP → production rollout
• Deploying enterprisecomplex systems integrations while solving critical business problems
• Drive rapid iteration while maintainingenterprise‐grade quality, security, and reliability

2. Agentic AI Solution Engineering

• Design and implementagentic 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 intoautonomous or semi‐autonomous agents
• Applyhuman‐in‐the‐loop,guardrails, andfallback mechanisms where required

3. Frontier Models & Context Engineering

• Work withfrontier foundation models (LLMs, multimodal models) and enterprise‐approved model stacks
• Performcontext engineering:

• • Prompt design and prompt chaining
  • Tool grounding and retrieval‐augmented generation (RAG)
  • Knowledge graph and memory integration

• Optimize solutions foraccuracy, latency, cost, and reliability

4. AI‐First Engineering & Developer Tooling

• LeverageAI‐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 oneffective human‐AI collaboration in engineering

5. Intelligent CI/CD & MLOps

• Design and implementintelligent CI/CD pipelines integrating:

• • AI‐generated code and test artifacts
  • Policy and control validation
  • Automated security and compliance checks

• Integrate agentic workflows intoDevSecOps / 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 atrusted technical advisor to customers and internal stakeholders
• Present architectures, demos, and outcomes toengineering 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 insoftware engineering (backend, APIs, distributed systems)
• Experience buildingproduction‐grade cloud‐native platforms/applications
• Proficiency in at least one modern programming language (Python, Java, Go, or similar)
• Solid understanding ofsystem design, scalability, and reliability
• Skilled inhyperscale platforms (AWS, GCP, Azure, OpenShift)

Agentic AI & AI Engineering

• Hands‐on experience withagentic AI frameworks and orchestration patterns (n8n, LangGraph, Semantic Kernel, CrewAI, etc.)
• Experience working withLLMs / foundation models in enterprise settings (Claude, Gemini, OpenAI)
• Strong skills inprompt engineering, context engineering, and tool integration
• Understanding ofRAG, memory systems, and knowledge grounding
• Spec driven development – Architecture, Security and Application frameworks.

AI Tooling & Productivity

• Practical experience usingCursor,GitHub Copilot, or similar AI coding tools
• Familiarity with AI‐assisted testing, documentation, and code review
• Ability to designAI‐first developer workflows

DevOps, CI/CD & Platform Integration

• Experience with CI/CD pipelines, infrastructure as code, and cloud platforms
• Understanding ofDevSecOps 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 inambiguous, fast‐moving environments
• Excellent communication skills with both technical and non‐technical stakeholders
• Customer‐centric mindset with ownership and accountability
• Good handle onComplex enterprisesystem integrations

Preferred Qualifications

• Experience inregulated industries (banking, financial services, healthcare, etc.)
• Exposure toAI governance, model risk, and compliance frameworks
• Prior experience incustomer‐facing engineering roles (FDE, Solutions Engineer, Field Engineer)
• Experience contributing toplatform blueprints, accelerators, or internal frameworks

What Success Looks Like

• Business ideas move to productionfaster and with higher confidence
• Agentic AI solutions delivermeasurable business outcomes
• Engineering teams adoptAI‐first workflows with strong governance
• Customers trust the platform and the FDE as astrategic delivery partner