Forward Deployed AI/ML Engineer IV

Role details

At E Source, we help utilities make sense of complexity in a rapidly changing landscape, and we’re looking for a Forward Deployed Engineer, AI/ML to help shape how that impact shows up in the world. E Source is a research, data/analytics, and technology focused professional services firm focused exclusively on the utility industry in the US and Canada. We help utilities target and serve their customers more effectively, enhance and optimize their grid, and leverage operating best practices and technologies to manage their business more effectively. Headquartered in Texas, we have 450+ employees across the US and Canada. Learn more at www.esource.com http://www.esource.com As a Forward Deployed Engineer, AI/ML, you’ll join our AI and Data Engineering (AIDE) team and embed directly with our utility clients to understand their hardest operational problems, then build and ship the systems that solve them: production code, in the client’s environment, used by their people, and measured against their outcomes. This role focuses on taking AI from a promising demo to a system a utility’s business runs on: grounded, evaluated, monitored, and trusted by the operators who depend on it. You’ll own engagements end to end, from technical discovery through architecture, build, deployment, and adoption, with no handoff points in between. Engagements move quickly: expect a useful system running in the client’s environment within weeks, then hardened toward production based on how their teams actually use it. You’ll work closely with client technical teams and executives, and with E Source’s machine learning engineers, data engineers, software engineers, and consultants, to deliver AI solutions for clients and bring field learnings back into our products and practices. In this role, you will: - Embed with utility clients to design, build, and deploy generative AI (GenAI) and machine learning (ML) systems that solve real operational problems in the client’s environment - Own the technical architecture of each engagement — retrieval, orchestration, model selection, serving, evaluation, and monitoring — and defend it to client architects and security teams - Run technical discovery independently, separate the problem as stated from the underlying need, and say so when the requested solution is the wrong one - Deliver a working, useful system early in each engagement, typically within the first few weeks, then iterate toward production hardening — tactical solutions are legitimate, but undocumented or unowned ones are not - Design task-specific evaluations before building, define what “correct” means with client subject matter experts, and hold every system to those evaluations before release - Build retrieval and grounding systems over client data, including structured, unstructured, and graph-backed knowledge sources - Integrate with client data platforms and business systems, including undocumented and legacy systems - Instrument systems so accuracy, latency, cost, and drift are measured rather than asserted, and hand off operations the client’s team can run without us - Build and deploy the operator-facing application layer — review and approval interfaces, agent and chat front ends, evaluation dashboards, and workflow tools — so delivered systems are usable by client teams, not just their engineers - Set realistic expectations with executives about what AI can and cannot do for their problem - Manage scope, timeline, and expectations against a defined statement of work, and raise risk early - Contribute reusable accelerators, evaluation harnesses, and reference architectures that scale across clients, and share implementation feedback with E Source’s engineering and product teams - Apply AI safety, data privacy, and governance controls appropriate to the utility regulatory context You’re likely a great fit if you: - Are comfortable owning an engagement end to end — from discovery through architecture, build, deployment, and post-go-live support — rather than a single phase of it - Navigate ambiguity, incomplete data, and objectives that shift mid-engagement without losing traction - Communicate clearly in writing and can whiteboard your thinking for both engineers and executives - Are candid about what didn’t work, why, and what you changed in your practice because of it - Move fast without cutting corners, are comfortable delivering a working system in weeks rather than quarters, and know what to defer - Work well with enterprise clients and can navigate stakeholders ranging from individual contributors to executives And even better if you: - Hold a master’s degree or PhD in a relevant quantitative field - Have built agent frameworks and tool-calling architectures in production - Bring knowledge graph or semantic layer experience for grounding and reasoning - Have fine-tuning, distillation, or prompt optimization experience — and the judgment to know when they’re not the answer - Bring classical ML experience (forecasting, ranking, anomaly detection) and know when to choose it over a large language model (LLM) - Are fluent in data engineering: Spark, pipeline design, and lakehouse architectures - Have built and deployed Databricks Apps, or comparable operator-facing applications on a modern frontend framework (React, Streamlit, Dash, or Gradio) with REST or GraphQL APIs - Hold Databricks or cloud AI/ML certifications Experience and Skills to Qualify Include: - Bachelor’s degree in computer science, engineering, statistics, or a related quantitative field - Five or more years of engineering experience, including generative AI (GenAI) or machine learning (ML) systems shipped to production and iterated on based on real usage - Expert proficiency in Python and working proficiency in at least one of TypeScript, Scala, or Java - Hands-on production experience with at least one major model provider or open-weights stack, and one vector or hybrid retrieval system - Experience building and applying evaluation fr