AI Enabled Operating Model - Forward Deployed Eng in at Snowbird Agility

Forward Deployed Engineer (AI-Native Transformation)

POSITION STATUS: CONTINGENT UPON CONTRACT AWARD / SECURING ANTICIPATED WORK

This position is contingent upon the company securing the anticipated work in January 2027. Recruitment and candidate consideration may take place before that decision; however, any employment offer, start date, and onboarding are dependent on the work being secured and authorized to proceed. January 2027 is the anticipated award/work decision timeframe, not a guaranteed start date.

Position Summary

We are seeking a Forward Deployed Engineer (FDE) to partner directly with clients to identify high-value opportunities for AI-native transformation, redesign workflows, and deliver practical AI-enabled solutions. This role combines hands-on engineering with consulting, discovery, facilitation, and change enablement. The FDE will prototype, integrate, evaluate, and operationalize AI capabilities while ensuring that solutions improve how people work, drive adoption, and produce measurable business outcomes.

· Client Consulting & Discovery: Lead client discovery sessions, stakeholder interviews, process walkthroughs, and collaborative workshops to understand goals, pain points, constraints, and success criteria.

· Stakeholder Engagement: Build trusted relationships with business leaders, frontline users, technical teams, and executive stakeholders; translate complex AI concepts into clear options, tradeoffs, and recommendations.

· Workflow Transformation: Map current-state processes and design future-state AI-native workflows, including thoughtful allocation of tasks between people, AI agents, and existing systems.

· AI Solution Engineering: Develop working prototypes and implement AI-enabled capabilities using Python, large language models (LLMs), retrieval-augmented generation (RAG), APIs, and integrations with enterprise tools and data.

· Agentic AI & Human Oversight: Design agentic AI workflows, prompt strategies, orchestration patterns, and human-in-the-loop review, escalation, and approval mechanisms appropriate to the risk and use case.

· Evaluation & Reliability: Define and execute AI evaluations for quality, accuracy, groundedness, safety, latency, and usefulness; test failure modes and continuously refine prompts, retrieval, and workflows.

· Agile Delivery: Work in Agile teams to plan, prioritize, prototype, iterate, demo, and deliver solutions; coordinate closely with product owners, delivery teams, security, and operational stakeholders.

· Implementation & Adoption: Integrate prototypes into client workflows and systems; support pilot rollouts, user feedback, enablement, operational handoff, and production deployment where appropriate.

· Outcomes & Value Realization: Establish baselines and success measures; track adoption, productivity, cycle time, efficiency, service quality, and other agreed outcomes; communicate realized value to stakeholders.

· Documentation & Governance: Document technical designs, data flows, assumptions, evaluation results, operational controls, and implementation decisions; support responsible AI and relevant security/privacy requirements.

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

· Strong hands-on software engineering experience using Python, APIs, and modern application or integration patterns.

· Practical experience building with LLMs, prompt engineering, RAG architectures, and evaluating AI-driven solutions.

· Experience developing prototypes and translating them into usable workflows or integrated applications.

· Demonstrated experience working directly with clients or business stakeholders through discovery, requirements clarification, facilitation, and solution delivery.

· Ability to analyze and redesign business processes with a focus on effective human-AI collaboration.

· Experience delivering solutions using Agile methods, including iterative planning, demonstrations, and stakeholder feedback.

· Ability to explain technical decisions, risks, and measurable outcomes to both technical audiences and executive stakeholders.

· Strong problem-solving, collaboration, documentation, and communication skills.

· Experience with agentic AI systems, tool use, orchestration, AI evaluation frameworks, and human-in-the-loop controls.

· Experience working in a Scaled Agile Framework (SAFe) environment; SAFe Practice Consultant (SPC) certification preferred, not required.

· Experience integrating AI into enterprise workflows, platforms, and legacy systems.

· Experience in consulting, client-facing delivery, digital transformation, or organizational change initiatives.

· Experience supporting healthcare, federal government, or other regulated environments.

· Familiarity with AI governance, security, privacy, observability, and deployment practices.

· Client-validated use cases with defined baseline metrics, target outcomes, and implementation priorities.

· Prototypes and AI-enabled workflows successfully tested with users and integrated into practical processes.

· Measurable improvements in adoption, productivity, efficiency, cycle time, quality, or other agreed business outcomes.

· Clear evidence of user trust, appropriate human oversight, solution reliability, and sustained stakeholder engagement.

· Timely, transparent communication of risks, tradeoffs, results, and recommended next steps.