Data & AI Engineering Lead

Intelligent Generation•Oak Brook, IL

3d•Hybrid

About The Position

Data & AI Engineering Lead Full Time | Hybrid Preferred | Chicago Metro Area Preferred Build the data and agent foundation for POWR:Suite Intelligent Generation’s mission is to empower businesses to engage the clean energy grid. Intelligent Generation builds and operates POWR:Suite , a software platform that helps battery energy storage assets make highly profitable economic decisions. POWR:Suite connects distributed energy assets to wholesale power markets while also optimizing behind-the-meter value: reducing utility bills, managing demand charges, improving asset performance, supporting resilience, and helping customers capture the full economic value of their energy assets. Our work sits at the intersection of energy markets, grid operations, customer savings, software automation, telemetry, and AI-assisted decision-making. We are looking for a data and AI engineering leader to build the data, retrieval, machine learning, and agent foundation that helps POWR:Suite scale with intelligence and control. This is a leadership role. You will be hands-on early, but the expectation is that you will grow into leading data and AI engineers, owning the technical roadmap, and orchestrating agents that support analysis, operations, engineering, settlement, reporting, customer value proof, and decision support.

Requirements

8+ years in data engineering, ML engineering, AI engineering, analytics engineering, or technical data product leadership
Experience leading technical work across teams or mentoring engineers
Strong GCP data platform experience, especially BigQuery, Pub/Sub, Dataflow, Cloud Storage, Vertex AI, or equivalent cloud-native services
Strong Python experience
Experience building production data pipelines and data platforms
Experience with RAG, LLM integration, vector search, retrieval evaluation, or AI-assisted knowledge systems
Ability to connect data architecture to business decisions, economic outcomes, and user workflows
Hands-on experience using AI tools as part of technical work
Ability to build, maintain, evaluate, govern, and orchestrate agents that support data, analytics, engineering, or operational workflows
Product-minded technical leader who asks what decision the data or AI system is supposed to improve

Nice To Haves

Energy market, grid operations, DER, BESS, utility, or energy management experience
Experience with operational telemetry, time-series data, customer savings analysis, or financial settlement data
Experience with agentic AI, tool use, eval harnesses, or multi-agent systems
MLOps experience including monitoring, drift detection, model evaluation, or experiment tracking
GCP Professional Data Engineer, ML Engineer, or Cloud Architect certification
Experience building or leading a data or AI engineering team

Responsibilities

Lead the architecture and evolution of IG’s data platform on GCP across BigQuery, Pub/Sub, Dataflow, Cloud Storage, Vertex AI, and related services.
Define standards for data quality, ownership, freshness, lineage, observability, and reliability across operational telemetry, market data, financial data, asset data, customer savings data, and customer reporting.
Build retrieval-augmented systems that ground agents in IG’s actual operating context: market rules, utility bill structures, demand charge logic, asset behavior, contracts, runbooks, incidents, settlement logic, customer commitments, and operational history.
Lead the development of models and analytical capabilities for anomaly detection, forecasting, performance monitoring, revenue variance explanation, customer savings analysis, operational risk detection, and decision support.
Build data and AI capabilities that help explain the economic value created by POWR:Suite, including market revenue, bill reduction, demand charge management, operational performance, and customer-facing proof of value.
Build, maintain, evaluate, and govern agents that support POWR:Suite workflows. Define what agents can access, what they produce, how their outputs are evaluated, and where human review is required.
Translate business workflows into data and AI requirements. Define what intelligence capabilities should be built, what success looks like, and how they improve business outcomes.
Over time, build and lead a data and AI engineering function. Establish how engineers, analysts, business users, and agents work together to improve speed, quality, explainability, and institutional learning.