AI Engineer, Agentic Systems

FuelCell Energy•Danbury, CT

1d•$100,000 - $115,000•Hybrid

About The Position

FuelCell Energy is redefining how the world produces and uses power. As a leading clean energy technology company, we're helping businesses, communities, utilities, and data centers meet growing energy demands with reliable, always-on power solutions. With nearly a gigawatt of fuel cell platforms deployed worldwide, we're accelerating the transition to a more resilient energy future. Join a team of innovators, problem-solvers, and industry pioneers who are tackling some of the world's most complex energy challenges while shaping the next generation of sustainable power. Your work here won't just power facilities. It will help power the future. We are currently seeking a highly technical AI Engineer, Agentic Systems to join our team to design, build, and deploy AI agents and applied AI solutions to achieve business outcomes. Reporting to the Senior Director, Infrastructure, Cybersecurity & AI, this hands-on engineer turns business needs into working, production-grade AI capabilities across corporate, manufacturing, and operational (OT) environments. This is a hybrid position based in Danbury, CT, with regular onsite presence to facilitate collaboration with cross-functional teams, understand operational workflows, and help ensure solutions are secure, reliable, and successfully adopted. The AI Engineer, Agentic Systems combines deep technical expertise with strong judgment about where AI adds durable value. The role designs multi-step agentic workflows, integrates large language models with enterprise data and tools, and delivers measurable outcomes while partnering closely with Cybersecurity, IT, and business stakeholders to meet established security, risk, and governance requirements. This individual will also help shape and guide the organization’s AI roadmap, identifying and prioritizing where AI can be responsibly applied to create value.

Requirements

Bachelor’s degree in computer science, software engineering, data science, or a related discipline. Extensive experience will be considered in lieu of a degree.
Five or more years of software or AI/ML engineering experience, including hands-on delivery of production AI or agentic solutions integrated with enterprise data and systems.
Demonstrable knowledge/experience in designing and building AI agents and agentic workflows, including multi-step reasoning, tool/function calling, and orchestration frameworks.
Developing LLM applications using RAG, prompt engineering, embeddings, vector databases, and evaluation of model outputs.
Strong software engineering fundamentals, including Python, APIs, version control, testing, and building maintainable, production-grade code.
Integrating AI solutions with enterprise data sources, applications, and platforms through APIs, connectors, and data pipelines.
Cloud AI services and platforms, particularly Microsoft Azure AI, and familiarity with Copilot and Copilot Studio for enterprise agent development.
Machine learning concepts and the practical tradeoffs of model selection, accuracy, cost, latency, and reliability.
AI safety, security, and governance practices, including data protection, access controls, guardrails, and responsible-AI principles.
Evaluating, monitoring, and improving deployed AI solutions, including observability, guardrails, and performance tuning.
Excellent communication skills with the ability to create clear documentation and explain technical AI concepts to non-technical stakeholders.
Strong judgment, structured problem-solving skills, and attention to detail, with a focus on delivering measurable business value.

Nice To Haves

Preferred certifications include Microsoft Azure AI, Azure AI Engineer, data science, machine learning, or a comparable AI/ML credential.
Occasional travel to company locations and availability outside of core business hours for critical solution or deployment events may be required.

Responsibilities

Design, build, and deploy AI agents and agentic workflows that automate multi-step tasks, reason over enterprise data, and take action through integrated tools and systems.
Translate business needs into practical AI solutions, from problem framing and feasibility through prototyping, implementation, evaluation, and transition to production.
Develop and integrate large language model (LLM) applications using techniques such as retrieval-augmented generation (RAG), prompt engineering, function/tool calling, and orchestration frameworks.
Build and maintain the supporting components for agentic systems, including data pipelines, vector stores, connectors to enterprise sources, APIs, and tool integrations.
Establish evaluation, testing, and guardrails for AI solutions, including accuracy, reliability, safety, cost, latency, and monitoring of model and agent behavior in production.
Partner with Cybersecurity to ensure responsible and secure AI, including data protection, access controls, prompt-injection and abuse mitigation, logging, and alignment to governance and compliance requirements.
Collaborate with IT, application, manufacturing, OT, and business teams to identify high-value use cases, gather requirements, and deliver solutions that are adopted and maintained.
Own the lifecycle of deployed AI solutions, including versioning, observability, performance tuning, prompt and model updates, and continuous improvement based on feedback and results.
Evaluate emerging AI models, agent frameworks, and platforms (including Microsoft Copilot and Copilot Studio) and recommend fit-for-purpose approaches that balance capability, cost, and supportability.
Create clear technical documentation, reusable patterns, and coaching so AI solutions are transparent, supportable, and repeatable across the organization.
Contribute to and sometimes lead AI and automation projects, providing technical leadership and hands-on delivery from design through operations.
Help define and guide the organization’s AI roadmap, translating business priorities into a practical, sequenced plan for where and how AI is adopted.
Identify, evaluate, and prioritize AI opportunities based on business value, feasibility, risk, cost, and readiness, and recommend build, buy, or defer decisions.
Establish reusable patterns, standards, and reference architectures that allow AI solutions to scale responsibly across the organization.
Track the evolving AI landscape-models, agent frameworks, and platforms-and advise leadership on emerging capabilities, risks, and their potential impact on the roadmap.
Provide clear, decision-ready updates to IT leadership and business stakeholders on AI progress, outcomes, risks, and recommended next steps.
Partner with business, manufacturing, OT, and functional teams to understand workflows, pain points, and objectives, and identify where AI can deliver meaningful value.
Lead use-case discovery and qualification, defining the problem, success measures, data availability, and expected business outcome before committing to a build.
Facilitate working sessions with stakeholders to shape requirements, set realistic expectations, and align on scope, value, and priority.
Distinguish high-value, feasible use cases from low-value or high-risk ones, and steer effort toward solutions that will be adopted and sustained.
Ensure delivered solutions are understood, adopted, and supported by the business through clear documentation, enablement, and follow-through.
Design and deploy AI agents using secure-by-design principles, including least-privilege access, data protection, input/output validation, and mitigation of prompt-injection, data-leakage, and abuse risks.
Build guardrails, logging, monitoring, and human-in-the-loop controls appropriate to the sensitivity and impact of each AI solution.
Partner with Cybersecurity to align AI solutions with established security, privacy, governance, and responsible AI requirements throughout the lifecycle.
Apply strong Azure architecture understanding (desirable) to deploy AI solutions on secure, well-architected, and cost-effective cloud foundations.
Leverage Azure AI services, identity, networking, and data controls to ensure AI agents are reliable, observable, and compliant in production.