Forward Deployed AI/ML Engineer IV

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

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 frameworks for large language model (LLM) and ML systems, including retrieval-augmented generation (RAG), agentic or multi-agent systems, structured extraction, or text-to-SQL

Experience with LLM operations (LLMOps): model serving, latency and cost management, prompt and model versioning, monitoring, and regression detection

Working knowledge of at least one major cloud ecosystem (Amazon Web Services (AWS), Azure, or Google Cloud Platform (GCP)); experience with AWS and Databricks strongly preferred

Proficiency with Git, Docker, and continuous integration and continuous delivery (CI/CD) tools

Experience using AI coding assistants and agentic development tools to rapidly prototype, test, and iterate on working software

Experience in the energy or utility industry, or another asset-heavy sector, with working familiarity of the systems, data, and operational constraints utilities face

Experience shipping on compressed timelines, from first conversation to a working system in weeks rather than quarters

At E Source, you’ll work alongside people who are thoughtful, curious, and deeply knowledgeable about utility and energy systems. We value clarity over jargon, substance over flash, and collaboration over ego. You’ll have the opportunity to grow your skills, contribute to meaningful work, and help shape a company that plays a real role in how the energy future unfolds.

What you can expect

Excellent insurance options, including medical, dental, and vision plans; company-paid life insurance; company-paid long- and short-term disability insurance; medical and dependent-care flexible spending plans, and paid parental leave.

A flexible time off (FTO) policy that provides paid time away from work, approved by your manager, while ensuring business needs, workload commitments, and appropriate coverage are maintained.

A 401(k) plan with a 3% employer match.

The budgeted salary for this position is $175,000–$200,000 USD + annual bonus. Ac