Forward Deployed AI Engineer

Resultant•Chicago, IL

About The Position

As a Forward Deployed Engineer, you will embed with client and Resultant teams to turn ambiguous, high-value problems into secure, production-ready agentic AI, data, and software solutions. You will move fluidly from discovery and rapid prototyping through architecture, implementation, deployment, adoption, and operational handoff. This is a hands-on engineering and consulting role. You will write code, make technical tradeoffs visible, work directly with users and leaders, and measure whether what we build improves a real decision, workflow, or outcome. You will leave clients stronger - not only with a working solution, but with the documentation, knowledge, and ownership needed to sustain it.

Requirements

At least 10 years of overall professional experience delivering technology solutions across cloud technologies and modern data platforms, including architecture, implementation, integration, deployment, and operational ownership.
At least 5 years of consulting experience working directly with clients across both public- and private-sector domains.
Demonstrated experience building and operating production solutions in software, data, ML, platform engineering, or a comparable technical discipline.
Strong understanding of the agent-building landscape, including modern agent frameworks, frontier and open models, model-selection tradeoffs, tool use, orchestration, state and memory, evaluation, guardrails, human-in-the-loop design, observability, security, and cost/latency considerations.
Hands-on experience designing, building, evaluating, and operating agentic systems with at least one modern framework. You should be able to explain why a particular framework or model is appropriate for a client's constraints rather than defaulting to a familiar tool.
Experience connecting agent capabilities to business or mission value: selecting the right workflows, establishing baselines and success measures, redesigning work where needed, enabling users, and driving sustained usage and adoption.
Strong fundamental understanding of programming constructs, software design, and application frameworks, with the judgment to select and structure languages, frameworks, repositories, interfaces, tests, and development tooling so coding agents can build, validate, debug, and maintain software efficiently and reliably.
Experience designing or integrating APIs, working with SQL and data models, and using version control, automated testing, and CI/CD practices.
Hands-on experience deploying to at least one major cloud platform - AWS, Azure, or Google Cloud - and working with core security, identity, networking, monitoring, and cost considerations.
Experience taking an AI, data, or software capability beyond a demo into reliable use, including evaluation, failure handling, observability, and operational ownership.
Evidence of working directly with clients, users, or cross-functional business stakeholders to discover needs, explain tradeoffs, and earn trust.
Ability to structure ambiguous problems, learn a client's domain quickly, balance speed with engineering quality, and follow through on commitments.
Clear written and verbal communication, including technical documentation and presentations for audiences with different levels of technical depth.
Sound judgment with sensitive data, privacy, security, accessibility, and responsible use of AI.

Nice To Haves

We care more about evidence that you can do this work than about a particular degree, pedigree, or exact vendor stack. If you meet most of the required capabilities and are excited by the mission, we encourage you to apply.

Responsibilities

Partner with client stakeholders and end users to understand the real problem, map workflows and constraints, and translate needs into user stories, technical requirements, and measurable success criteria.
Identify high-value AI, agentic, data, and application use cases; distinguish when an agent is - or is not - the right solution; define the value case and adoption measures; test assumptions quickly through working prototypes; and recommend whether to stop, refine, productionize, or scale.
Design and build agentic systems, selecting appropriate models and orchestration patterns and implementing tool use, state and memory, evaluation, guardrails, human oversight, observability, security, and failure handling.
Design and build end-to-end solutions across APIs, applications, data pipelines, analytics, AI/ML capabilities, system integrations, and cloud services, selecting technology based on client needs rather than vendor preference.
Turn promising prototypes into dependable production systems through testing, automation, security and privacy controls, responsible AI practices, observability, performance, cost awareness, and operational runbooks.
Lead technical workstreams, break ambiguous goals into executable increments, surface risks early, and deliver reliably within engagement commitments.
Communicate architecture, tradeoffs, progress, and results clearly to technical and nontechnical audiences, including candid and respectful recommendations when evidence points to a different path.
Work side by side with client teams through demos, pairing, documentation, training, workflow redesign, and change enablement so users adopt the solution and internal owners can operate it. Use feedback, usage signals, and outcome measures to improve adoption after launch.
Collaborate with Resultant's data, cloud, application, security, product, change, project delivery, and industry experts to bring the right perspectives to each challenge.
Capture repeatable patterns from engagements as reusable code, reference architectures, accelerators, or playbooks that improve quality and speed across future work.