Data & AI Engineering Lead

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. Why this role matters IG's ability to scale depends on more than adding assets. We need trusted data, reusable knowledge, reliable pipelines, governed agents, and decision-support systems that help teams operate faster and with more confidence. Every battery decision has an economic impact: market revenue, bill savings, demand charge reduction, asset performance, resilience, customer reporting, and settlement confidence. You will help turn telemetry, market data, utility bill logic, demand charge rules, operational workflows, settlement logic, customer commitments, and institutional knowledge into a durable advantage for POWR:Suite. What you will lead and own Data platform architecture Lead the architecture and evolution of IG's data platform on GCP across BigQuery, Pub/Sub, Dataflow, Cloud Storage, Vertex AI, and related services. Data quality, lineage, and contracts 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. RAG and knowledge systems 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. AI and ML capabilities 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. Economic intelligence 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. Agent design and governance 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. Data and AI product leadership 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. People and agent orchestration 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. What success looks like First 90 days

• Understand IG's current data sources, pipelines, dashboards, models, reports, economic calculations, and knowledge systems
• Map key data flows across telemetry, dispatch, settlements, customer bill savings, operations, and customer reporting
• Identify priority gaps in data quality, retrieval quality, economic visibility, and agent readiness Define a practical data and AI roadmap for POWR:Suite

First 6 months

• Own selected production data pipelines and retrieval systems
• Improve data quality, lineage, observability, and documentation
• Establish evaluation patterns for RAG and agent outputs Build or improve agents that support analysis, settlement, reporting, customer value proof, operations, or engineering workflows

First 12 months

• Lead the data and AI engineering function for POWR:Suite
• Build a reusable data and knowledge foundation
• Improve confidence in settlement, performance, operational intelligence, bill savings analysis, and customer reporting Mature agent-assisted workflows that help teams work faster with better control

Do you have experience in Team development?, * 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

Strongly preferred

• 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

Pulled from the full job description

• 401(k)
• Health insurance
• 401(k) matching
• Paid time off
• Work from home, * Warrants
• Annual bonus
• 401(k)
• 401(k) matching
• Competitive salary
• Health insurance
• Paid time off