Artificial Intelligence & Automation Engineer

Sargent & Lundy•Chicago, IL

1d•Hybrid

About The Position

Sargent & Lundy is a leading consulting engineering firm specializing in the power and energy sectors. Since 1891, we have provided comprehensive engineering, design, and consulting services for both traditional and renewable power generation, grid modernization, nuclear power, and beyond. Our mission is to help clients achieve their energy goals effectively by leveraging advanced technologies and adopting sustainable practices. We are seeking an AI & Automation Developer to join our Enterprise Data & Analytics team. This role is central to our growing AI/Automation capability and will serve as a hands-on technical partner to business groups across the organization. The ideal candidate brings demonstrated experience in both artificial intelligence and process automation, with a proven ability to assess feasibility, design solutions, and deliver measurable outcomes that create efficiencies and solve real business problems. As an AI & Automation Developer, you will work directly with business stakeholders to understand their challenges, evaluate whether AI, automation, or a combination of both is the right approach, and then design and deliver end-to-end solutions. This is a growth-oriented role for those passionate about using AI and automation to drive impact in the power and energy sector.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
3+ years of professional experience developing and deploying AI/ML solutions and automation/RPA solutions in a business environment.
Demonstrated experience across both AI/ML (e.g., TensorFlow, PyTorch, scikit-learn, LLMs) and process automation (e.g., RPA, Power Automate, scripting, workflow tools).
Proven ability to work directly with business stakeholders to assess feasibility, gather requirements, and translate business problems into technical solutions.
Strong proficiency in software development (Python preferred), with hands-on experience building production-grade applications.
Familiarity with professional software development workflows: Git-based source control, branching strategies, code review, and effective team collaboration on shared codebases.
Experience writing and maintaining automated tests, debugging, and software troubleshooting.
Exposure to agile development methodologies (Scrum, Kanban), CI/CD practices, and collaborative development environments.
Basic experience deploying cloud services and APIs for AI workloads (AWS, Azure, or Google Cloud).
Understanding of machine learning workflow: data preparation, model training, evaluation, and deployment, alongside best practices in software delivery.
Strong analytical and problem-solving capabilities with a demonstrated ability to assess business processes and identify automation opportunities.
Excellent communication skills for explaining technical information to both technical and non-technical audiences.
Ability to adapt to evolving technology landscapes and work collaboratively in multidisciplinary teams.

Nice To Haves

Experience with generative AI, large language models (LLMs), prompt engineering, and AI agent frameworks.
Experience with enterprise integration patterns, APIs, and connecting disparate business systems.
Familiarity with data engineering concepts (ETL/ELT, data pipelines, data warehousing).
Experience in the power, energy, nuclear, engineering, or professional services industry.
Experience conducting structured opportunity assessments, business case development, or cost-benefit analysis for technology initiatives.
Certifications in AI/ML, RPA (e.g., UiPath, Microsoft Power Platform), or cloud platforms (AWS, Azure, GCP).
Knowledge of data governance, responsible AI principles, and regulatory compliance for sensitive or regulated industries.

Responsibilities

Partner directly with business groups to identify pain points, inefficiencies, and opportunities for AI and automation solutions.
Conduct structured feasibility assessments for proposed initiatives, evaluating technical viability, data readiness, integration complexity, expected ROI, and organizational readiness.
Translate business problems into clearly defined technical requirements, solution approaches, and delivery plans.
Present findings, recommendations, and solution options to both technical and non-technical stakeholders, ensuring alignment on scope, value, and approach.
Contribute to the intake and prioritization process for AI/automation demand across business groups, helping shape the portfolio backlog.
Research, design, and develop AI-driven solutions and software applications for internal and client-facing needs.
Develop and deploy natural language processing (NLP), computer vision, generative AI, and other AI capabilities as applicable to business use cases.
Implement retrieval-augmented generation (RAG) patterns, prompt engineering strategies, and AI agent architectures to deliver intelligent automation solutions.
Ensure robust, high-quality data for model training and software features by building validation checks and monitoring systems.
Document model lineage, decision processes, and software dependencies; continually validate performance against business objectives.
Design and implement automation solutions using RPA tools, scripting, workflow platforms, and low-code/no-code technologies (e.g., Power Automate, UiPath, or similar).
Identify and automate repetitive business processes, boosting productivity and reliability across teams.
Develop integration solutions that connect enterprise systems, APIs, and data sources to enable end-to-end automated workflows.
Monitor, maintain, and optimize deployed automations to ensure sustained performance and reliability.
Develop production-grade code for automation, analytics, and user interfaces, ensuring scalability, reliability, and maintainability.
Use rigorous software development practices—clear source control (Git), code review cycles, effective documentation, modular code organization, and adherence to coding standards.
Implement robust automated testing (unit, integration, system tests) for both AI and broader software solutions, contributing to high code quality and continuous delivery.
Follow a disciplined software development lifecycle (SDLC): requirements analysis, design, development, testing, deployment, and maintenance.
Lead or participate in post-mortems to identify root causes of incidents and implement lessons learned in future releases.
Work closely with engineers, analysts, and IT to identify business problems that can be solved through AI or enhanced by automation.
Collaborate with multidisciplinary teams—including data scientists, data engineers, business analysts, and subject matter experts—to deliver comprehensive solutions.
Act as a bridge between technical and non-technical teams, helping stakeholders understand how software and AI solutions deliver business value.
Participate actively in agile sprint planning, collaborating across functions to align releases with business needs.
Support responsible AI and software principles, including transparency, data privacy, and bias mitigation.
Adhere to S&L’s AI governance frameworks, ensuring all solutions comply with security, data privacy, and regulatory requirements.
Contribute to thorough documentation of system design, testing, deployment, and ongoing auditing to ensure traceability.
Share AI and automation best practices with colleagues, contributing to mentoring and team-wide knowledge growth.
Stay current on evolving trends in AI/ML, RPA, cloud computing, generative AI, and agentic AI; propose new tools and methodologies to improve team output.
Support leadership in strategic planning through technical insights from prototypes, pilots, and production deployments.
Contribute to the selection, integration, and long-term maintenance of cloud-based AI and software services, prioritizing security and system performance.