About this opportunity
Madison-Davis, LLC lists this Forward Deployed Engineer Level II opportunity in chicago, Illinois. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Our client is an enterprise financial services organization operating across multiple business lines and regulatory jurisdictions. The firm is investing heavily in modernizing how its corporate functions run — replacing manual process with automated, well-instrumented systems, and building internal products that its own people actually want to use. Technology here is treated as a core capability, not a support function, and engineers work close to the business rather than behind an intake queue.
Role Overview
As a Forward Deployed Engineer, you will work at the point where business need meets technical possibility — embedded directly with corporate function leaders, product managers, and platform engineering teams to design, prototype, and ship solutions to high-priority problems.
This is not a role built around long release cycles and detailed upfront specifications. You will be expected to move from a first conversation to a working prototype in days, then harden that prototype to enterprise standards for security, auditability, and scale. Success depends on an unusual combination: real depth as a hands-on engineer across applied AI and full-stack development, paired with the credibility and communication skills to earn trust from senior business stakeholders and drive alignment without formal authority. You should be as comfortable sketching an integration architecture with a line-of-business head in the morning as you are writing the code for it that afternoon.
This is the Level II role, carrying architecture ownership and mentorship responsibility in addition to hands-on delivery. Your focus will center on the corporate application landscape — CRM, HCM, ITSM, ERP, and the internal products built on top of them.
Key Responsibilities
Solution Design & Architecture
Own solution design for complex, cross-functional application and data problems, from discovery through technical blueprint
Define architecture decisions and articulate the trade-offs behind them to both engineering peers and business leadership
Design modular systems that move quickly without abandoning enterprise expectations for reliability, security, and maintainability
Contribute to architecture and security reviews, ensuring solutions align with established enterprise patterns and regulatory obligations
Produce clear technical artifacts: architecture diagrams, data flow maps, API contracts, and written solution briefs
Rapid Prototyping & Delivery
Build working prototypes for ambiguous business problems on compressed timelines, typically days to weeks
Convert loosely defined business requirements into concrete technical scope with minimal direction
Deliver prototypes that are built to survive production, not to be thrown away after the demo
Iterate continuously against direct feedback from business owners, product managers, and end users
Build clean front-end interfaces and dashboards that make data and automation outputs usable for non-technical audiences
Apply strong product and UX instincts to simplify complicated workflows rather than reproduce them in software
Applied AI & Automation
Design, build, and deploy AI agents and automated workflows that carry business processes end to end
Develop reusable capabilities and tooling that compose into larger automated pipelines
Connect models and agents to enterprise systems, APIs, and data sources through modern integration patterns, including Model Context Protocol servers and clients
Build evaluation harnesses, guardrails, and monitoring to keep automated systems reliable, explainable, and defensible under audit
Integrate LLMs, retrieval systems, and ML models into production workflows within a regulated environment
Track the applied AI landscape closely and bring credible new techniques and tooling into the team
Stakeholder Partnership & Influence
Embed with corporate function, product, and engineering teams to define problems jointly and deliver against them together
Shape technical direction and build cross-team alignment without relying on reporting lines
Communicate technical concepts clearly to non-technical stakeholders, in writing, in working sessions, and in executive forums
Mentor engineers on patterns for rapid delivery, applied AI development, and stakeholder-facing work
Contribute to a low-ego, high-ownership team culture where speed and quality are treated as compatible
Qualifications
Critical Skills
8+ years of professional software engineering experience, including meaningful time in a stakeholder-facing or embedded delivery capacity
Demonstrated ability to architect systems end to end — requirements through production deployment — with the documentation and communication to match
Hands-on experience building AI agents, including tool use, memory, and multi-step reasoning
Practical use of AI-assisted engineering tooling (Claude Code, Codex, or equivalent) as part of a daily workflow
Experience deploying and operating agents in production on a major platform (AWS Bedrock / AgentCore, Azure AI Foundry, or equivalent)
Working knowledge of Model Context Protocol, including building or consuming MCP servers to connect agents to enterprise systems
Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel
Strong Python plus at least one modern front-end framework (React, Vue, or Angular)
Cloud platform experience (AWS, Azure, or GCP)
Exceptional communication and interpersonal skills — you build trust quickly, operate well in ambiguity, and align people who don't report to you
Comfort with shifting priorities and high ownership expectations
Desirable Skills
Background in financial services, banking, insurance, or asset management — particularly in regulated data environments (SOX, GLBA, PCI, or model risk governance)
Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), and semantic search
Prompt engineering, fine-tuning, and LLM evaluation experience
Agent observability and tracing tooling (LangSmith, Arize, Weights & Biases, or similar)
Containerization and CI/CD practice (Docker, Kubernetes, GitHub Actions)
Hands-on work with enterprise SaaS and iPaaS platforms (Salesforce, Workday, ServiceNow, MuleSoft, Boomi, Workato)
Experience facilitating technical discovery workshops, design sprints, or architecture reviews
Worksite address
chicago, IL, 60290, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.