Job Details

AI Optimization Engineer

Location: Charlotte, NC (Hybrid, 3 days onsite at Optimist Hall Location) or Remote within the U.S. (must be able to work EST hours)

Compensation: $80 - $95/hour plus benefits

Duration: 12-month contract with strong potential for extension or conversion to full-time employment

Employment Requirements: W-2 only. No Corp-to-Corp or 1099 arrangements. Successful candidate must be able to work on W-2 without visa sponsorship both now and in future.

ABOUT THE ROLE

Our Client is seeking an AI Optimization Engineer to develop and support the optimization engine at the core of their Capital Investment Planning & Prioritization Tool (CIPT). This tool empowers planning teams to make data-driven decisions about which capital investments to fund, when, and why, by transforming complex business rules into robust, explainable, and auditable optimization models. The role involves designing, building, testing, and maintaining advanced mathematical optimization models—primarily using Mixed-Integer Linear Programming (MILP)—and integrating predictive analytics and benefit measures into portfolio planning. You will work closely with data and ML engineers, leverage cloud-based solutions (primarily AWS), and utilize modern software development practices, including AI-enabled tools like GitHub Copilot and Spec-Driven Development. The ideal candidate combines strong optimization and software engineering fundamentals with a willingness to learn new AI-driven approaches, ensuring solutions are mathematically correct, reproducible, and transparent for planners, finance partners, and regulators.

WHAT YOU'LL DO

• Translate complex capital planning business rules—including budget, resource, cash-flow, jurisdictional, and portfolio constraints—into mathematical optimization models.
• Formulate, implement, and tune Mixed-Integer Linear Programming (MILP) models using Python frameworks (e.g., Pyomo, PuLP, OR-Tools) and commercial/open-source solvers (e.g., Gurobi, CPLEX, HiGHS, CBC).
• Integrate predictive model outputs (e.g., Probability of Failure, Remaining Useful Life) and benefit measures (e.g., reliability, safety, financial, NPV) into optimization objectives for portfolio planning.
• Design, build, and maintain scenario optimization workflows, including scenario creation, copying, optimization across planning cycles, and deterministic run harnesses for reproducible results.
• Develop diagnostic and infeasibility analysis capabilities (e.g., constraint relaxation, irreducible infeasible subsets, clear error messages) to help planners understand optimization outcomes.
• Evaluate and recommend solvers and modeling approaches based on solution quality, run-time performance, licensing cost, and operational fit.
• Package and deploy optimization workloads as scalable cloud services (e.g., AWS Batch, Fargate, Lambda, Step Functions) with REST APIs, leveraging Infrastructure as Code tools (e.g., Terraform).
• Collaborate with data and ML engineers to integrate asset-health predictions and ensure seamless data flow into optimization models.
• Build automated test suites for optimization models, including unit tests for constraints, regression tests, reproducibility checks, and comparisons against historical results.
• Profile and tune model performance using reformulation, tighter bounds, decomposition, warm starts, and solver parameter tuning to meet run-time and scalability targets.
• Produce clear, explainable outputs for planners and stakeholders, including binding constraints, shadow prices, scenario comparisons, and investment selection rationale.
• Document model formulations, assumptions, and limitations for business owners, auditors, and future engineers.
• Contribute reusable optimization components, model templates, test harnesses, and engineering accelerators for broader team and organizational use.
• Utilize GitHub, GitHub Copilot, and AI agents to enhance software development, testing, and delivery processes; gain hands-on experience with agentic software engineering and automation workflows.
• Prepare, validate, and troubleshoot large input datasets and database integrations (e.g., PostgreSQL) for optimization models, ensuring data quality and version control.
• Participate in technical learning activities, communities of practice, hackathons, and innovation events to continuously expand cloud, AI, and optimization engineering skills.

WHAT YOU BRING

• Associate's degree in Information Technology or related field required; Bachelor's or Master's in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field preferred.
• 5+ years of relevant work experience in optimization, software development, or decision-support applications.
• Hands-on experience designing, building, and supporting optimization models, especially Mixed-Integer Linear Programming (MILP) using frameworks like Pyomo, PuLP, OR-Tools, or Gurobi Python API.
• Proficiency with commercial or open-source solvers (e.g., Gurobi, CPLEX, HiGHS, CBC) and strong understanding of linear and integer programming, including advanced formulation techniques (e.g., binary selection variables, logical/indicator constraints, big-M, multi-period timing, precedence constraints).
• Experience modeling knapsack-style and resource-constrained selection or scheduling problems, multi-objective or weighted-objective optimization, and tradeoff analysis.
• Ability to diagnose and improve optimization models (e.g., infeasibility, unboundedness, performance tuning, solver settings, reformulation, bounds tightening, decomposition).
• Strong Python programming skills for optimization, data processing, and service development.
• Experience developing applications or services on AWS or similar cloud platforms, with familiarity in compute-intensive and batch workloads (e.g., AWS Batch, Fargate/ECS, Lambda, Step Functions, S3, RDS, SageMaker).
• Familiarity with Infrastructure as Code tools (e.g., Terraform) and modern application architectures (APIs, containers, serverless, event-driven, distributed systems).
• Experience with Git and GitHub for source control and collaborative development, and interest in AI-enabled development tools (e.g., GitHub Copilot, AI agents, Spec-Driven Development).
• Experience working with relational, NoSQL, or cloud-based databases (PostgreSQL preferred), including data modeling, validation, and troubleshooting application/data-access issues.
• Understanding of determinism and reproducibility in solver-based systems (e.g., MIP gaps, tolerances, threads, seeds), and familiarity with sensitivity analysis, duals, and shadow prices.
• Strong communication skills and ability to explain technical concepts and optimization results to business users, planners, and stakeholders.
• Experience with capital planning, portfolio optimization, asset management, or utility planning domains is a plus.
• Experience contributing to reusable software components, frameworks, templates, or shared engineering solutions is a plus.
• Participation in technical learning activities, communities of practice, hackathons, or innovation events is a plus.
• Willingness to expand cloud engineering and AI-enabled development skills as part of the role.
• Familiarity with financial evaluation concepts (e.g., present value, NPV, escalation, discounting) and predictive modeling outputs (e.g., probability of failure, remaining useful life, risk scoring) is a plus.

Eight Eleven Group provides equal employment opportunities (EEO) to all employees and

applicants for employment without regard to race, color, religion, national origin, age, sex,

citizenship, disability, genetic information, gender, sexual orientation, gender identity, marital

status, amnesty, or status as a covered veteran in accordance with applicable federal, state, and

local laws.

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