Forward Deployed AI/ML Engineer IV

Role overview This is a senior, client-facing engineering position focused on deploying production-grade generative AI and machine learning systems inside utility organizations. The work spans the full engagement lifecycle, from technical discovery through architecture, build, deployment, and post-launch support, with the goal of turning AI prototypes into trusted, monitored systems used by real operators. It suits an engineer who thrives on ownership, ambiguity, and shipping software that measurably improves a client's operations.

Responsibilities - Embed directly with client engineering and leadership teams to scope problems, design architecture, and deploy AI/ML solutions into their production environments. - Lead the technical design across retrieval, orchestration, model selection, serving, evaluation, and monitoring, and defend those decisions to client architects and security stakeholders. - Build and integrate retrieval-augmented systems over structured, unstructured, and graph-backed data sources, including legacy platforms with limited documentation. - Define task-specific evaluation criteria with subject matter experts before development, instrument systems for accuracy, latency, cost, and drift, and harden solutions toward production after early working prototypes. - Construct operator-facing applications such as review tools, chat interfaces, agent front ends, evaluation dashboards, and workflow utilities so non-engineers can actually use the delivered systems. - Manage scope, timeline, and executive expectations against a defined statement of work, contribute reusable accelerators and reference architectures, and apply safety, privacy, and governance controls appropriate to regulated environments.

Requirements - Bachelor's degree in computer science, engineering, statistics, or a related quantitative field. - Five or more years of engineering experience, including generative AI or ML systems shipped to production and refined based on real usage. - Expert proficiency in Python plus working proficiency in at least one of TypeScript, Scala, or Java. - Hands-on production experience with a major model provider or open-weights stack and a vector or hybrid retrieval system. - Experience designing and applying evaluation frameworks for LLM and ML systems, including retrieval-augmented generation, agentic architectures, structured extraction, or text-to-SQL. - Working knowledge of at least one major cloud platform (AWS, Azure, or GCP), AWS and Databricks strongly preferred, along with Git, Docker, and CI/CD tooling.

Nice to have - Master's degree or PhD in a relevant quantitative field. - Production experience building agent frameworks and tool-calling architectures. - Familiarity with knowledge graphs, semantic layers, fine-tuning, distillation, prompt optimization, or classical ML approaches such as forecasting and anomaly detection. - Background in energy, utilities, or another asset-heavy sector with understanding of operational constraints. - Cloud or Databricks AI/ML certifications and experience with Databricks Apps or comparable React, Streamlit, Dash, or Gradio frontends.

Benefits and work setup - Remote-based role with significant travel to client sites, generally 30 to 50%, including extended on-site periods during discovery, deployment, and go-live. - Budgeted salary of $175,000 to $200,000 USD plus an annual bonus, adjusted for experience. - Medical, dental, and vision plans, company-paid life insurance, short- and long-term disability coverage, flexible spending accounts, paid parental leave, and a flexible time off policy. - 401(k) plan with a 3% employer match. - Authorization to work in the US or Canada is required; visa sponsorship is not available for this role.