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E Source

Forward Deployed AI/ML Engineer, IV

E Source

. Embed with utility clients to design, build, and deploy GenAI and ML systems in client environments .

Posted 11/2/2026full-timeRemote • United States, CanadaMid-LevelSenior💰 $175,000 - $200,000 per yearWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing, building, and deploying GenAI and ML systems, with a strong focus on technical architecture, evaluation frameworks, and integration with client data platforms. Proficient in managing project timelines and expectations while ensuring compliance with AI safety and governance standards in the utility sector.

Highest-signal resume keywords
Expert Proficiency In PythonExperience With GenAI Or ML SystemsHands-On Production Experience With Major Model ProvidersWorking Knowledge Of AWS, Azure, Or GCPExperience In The Energy Or Utility Industry

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
GenAI SystemsML SystemsEvaluation FrameworksModel ServingRetrieval SystemsPythonTypeScriptScalaJavaClassical ML
Soft Skills
Client ManagementExpectation SettingRisk ManagementCollaborationCommunication
Tools & Technologies
GitDockerCI/CD ToolsDatabricksAI Coding Assistants
Certifications & Qualifications
Cloud AI/ML Certifications
Industry Keywords
Utility IndustryEnergy SectorData PrivacyGovernance ControlsAsset-Heavy Sector

Tech Stack

Tools & technologies
AWSAzureCloudDockerGoogle Cloud PlatformJavaPythonScalaSparkSQLTypeScript

About the role

Key responsibilities & impact
  • Embed with utility clients to design, build, and deploy GenAI and ML systems in client environments
  • Own technical architecture, including retrieval, orchestration, model selection, serving, evaluation, and monitoring
  • Run technical discovery and identify underlying client needs
  • Deliver useful working systems within the first few weeks and iterate toward production hardening
  • Design task-specific evaluations and define correctness with client subject matter experts
  • Build retrieval and grounding systems over structured, unstructured, and graph-backed data
  • Integrate with client data platforms and business systems, including legacy systems
  • Instrument systems to measure accuracy, latency, cost, and drift
  • Build and deploy operator-facing applications, interfaces, dashboards, and workflow tools
  • Set realistic expectations with executives about AI capabilities
  • Manage scope, timelines, and expectations against statements of work; raise risks early
  • Contribute reusable accelerators, evaluation harnesses, and reference architectures
  • Share field feedback with E Source engineering and product teams
  • Apply AI safety, data privacy, and governance controls for the utility regulatory context
  • Provide post-go-live support and enable client teams to operate delivered systems

Requirements

What you’ll need
  • Bachelor’s degree in computer science, engineering, statistics, or a related quantitative field
  • Five or more years of engineering experience, including GenAI or ML systems shipped to production and iterated on based on real usage
  • Expert proficiency in Python
  • 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 LLM and ML systems, including RAG, agentic or multi-agent systems, structured extraction, or text-to-SQL
  • Experience with LLMOps, including model serving, latency and cost management, prompt and model versioning, monitoring, and regression detection
  • Working knowledge of at least one major cloud ecosystem: AWS, Azure, or GCP
  • Proficiency with Git, Docker, and CI/CD tools
  • Experience using AI coding assistants and agentic development tools
  • Experience in the energy or utility industry, or another asset-heavy sector
  • Experience shipping on compressed timelines, from first conversation to a working system in weeks rather than quarters
  • Applicants must be authorized to work for any employer in the US or Canada
  • Willingness to travel to client sites, generally 30 to 50%
  • Master’s degree or PhD in a relevant quantitative field is an additional advantage
  • Production agent frameworks and tool-calling architectures are an additional advantage
  • Knowledge graph or semantic layer experience is an additional advantage
  • Fine-tuning, distillation, or prompt optimization experience is an additional advantage
  • Classical ML experience is an additional advantage
  • Spark, pipeline design, and lakehouse architecture experience is an additional advantage
  • Databricks Apps or comparable operator-facing application experience is an additional advantage
  • Databricks or cloud AI/ML certifications are an additional advantage

Benefits

Comp & perks
  • 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
  • Paid parental leave
  • Flexible time off (FTO) policy
  • 401(k) plan with a 3% employer match
  • Annual bonus
  • Remote-based work arrangement
  • Meaningful work and opportunities to grow skills
  • Global/client travel opportunities