Job Description Current Need The US Oncology Network is seeking a Lead Forward Deployed Engineer (FDE) to partner directly with business and operational teams to turn complex, high-value problems into validated AI-enabled solutions. This is a hands-on, business-embedded engineering role for someone who can work comfortably in ambiguity, connect business needs with technology, and rapidly move ideas from discovery to working prototypes.

The FDE leads the front end of the AI delivery lifecycle. You will understand workflows, frame problems, assess technical options, build and test prototypes, and validate value with users. Once a concept is validated, you will define a transparent production path and work with AI Application Engineering, Data Engineering, MLOps, architecture, security, compliance, and operational teams to transition the solution for industrialization and scale.

This role requires strong software engineering judgment, broad technical fluency, and exceptional stakeholder engagement. The FDE role provides technical coherence from problem through prototype, preserves the intended business outcome, and ensures downstream teams have the context needed to deliver a scalable, governed solution.

Key Responsibilities

Embed with practice and business teams to understand workflows, user needs, operational constraints, pain points, and desired outcomes.

Lead discovery workshops, workflow assessments, and problem-framing sessions with executives, physicians, clinical teams, revenue cycle leaders, operational teams, and subject matter experts.

Translate ambiguous business needs into clear problem statements, testable hypotheses, technical options, prototype scopes, and measurable success criteria.

Advise stakeholders where AI, automation,



data, or workflow technology is appropriate and clearly communicate capabilities, limitations, risks, and tradeoffs.

Design pragmatic solution approaches using approved enterprise technologies, architecture patterns, and available data assets.

Build working prototypes, proofs of concept, and thin-slice solutions that test feasibility and enable meaningful feedback from representative users.

Apply sound software engineering practices so prototype code, interfaces, and design decisions can be understood, reused, or extended by delivery teams.

Use AI-enabled patterns such as retrieval-augmented generation, copilots, agents, semantic search, decision support, and workflow automation when appropriate to the problem.

Demonstrate prototypes, gather user and technical feedback, iterate rapidly, and recommend whether to stop, refine, or advance the work.

Identify application, data, integration, security, architecture, operational, and governance dependencies early in the engagement.

Collaborate with AI Application Engineers, Data Engineers, MLOps Engineers, architects, product leaders, security, compliance, and business SMEs to shape an executable delivery path.

Define solution requirements, dependencies, and acceptance criteria for production delivery.

Assess production readiness and document risks, decisions, and required controls.





Lead solution handoff to engineering teams and support successful implementation.

Drive reuse by capturing patterns, assets, and lessons learned across engagements.

Contribute to FDE playbooks, prototype standards, reusable asset libraries, and delivery practices that improve speed and consistency.

Mentor engineers in business discovery, problem framing, rapid prototyping and technical stakeholder communication.

Minimum Requirement

Degree or equivalent experience and typically 8+ years of relevant experience in software engineering, solution engineering, solution architecture, technical consulting, enterprise application delivery, or digital transformation.

Education

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.

Critical Skills

8+ years of experience building, integrating, troubleshooting, or delivering enterprise software solutions.

Strong hands-on full-stack engineering and troubleshooting experience across modern web applications, APIs, data, AI services, identity, security and cloud infrastructure.

Hands-on experience with React, Node.js, TypeScript, Python, and modern web application architecture.

Proficiency in API diagnostics and browser-based troubleshooting, including network requests, authentication, application state, errors and performance issues.

Hands-on experience with Snowflake and Databricks, including data pipelines, lineage, reconciliation, data quality and performance tuning.

Experience using logs, metrics, traces, dashboards and alerting to perform root-cause analysis and validate e

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