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# Forward Deployed AI/ML Engineer IV

- **Company:** Eliassen Group
- **Location:** Harrisburg, PA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $170,000 - $190,000
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Application Layers, Microsoft Azure, Business Systems, Information Engineering, Programming Tools, Graph Database, Information Extraction, Python (Programming Language), Machine Learning, Cloud Services, Software Safety, SQL Databases, TypeScript, Workflow Management Systems, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Model Validation, AI Coding Agents, Agentic-AI, Git, Data Layers, Build Management, Information Technology, Low Latency, Data Management, Machine Learning Operations, Front End Software Development, Software Version Control, Docker, Legacy Systems, Databricks
- **Published:** November 14, 2026
- **Apply:** https://dejobs.org/x/x/56206C51629E48138F8CC1DE49D20D7A/job/

## About the Role

* Bachelor's degree in computer science, engineering, statistics, or a related quantitative field.
 * Five or more years of engineering experience with GenAI or 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 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). AWS and Databricks preferred.
 * Proficiency with Git, Docker, and CI/CD tools.
 * Experience using AI coding assistants and agentic development tools to rapidly prototype, test, and iterate.
 * Experience in the energy or utility industry, or another asset-heavy sector, with familiarity with systems, data, and operational constraints utilities face.
 * Experience shipping on compressed timelines from first conversation to a working system in weeks.
 * Desired: utility experience, knowledge graphs or semantic layers, agent frameworks and tool-calling in production, fine-tuning or prompt optimization, classical ML experience, data engineering with Spark and lakehouse architectures, Databricks Apps or modern front-end frameworks, and relevant cloud or Databricks certifications.

Education Requirements:

 * Bachelor's degree in computer science, engineering, statistics, or a related quantitative field. Advanced degrees are a plus.

## Description

* Embed with utility clients to design, build, and deploy generative AI (GenAI) and machine learning (ML) systems that solve operational problems in the client's environment.
 * Own the technical architecture for retrieval, orchestration, model selection, serving, evaluation, and monitoring, and defend it to client architects and security teams.
 * Run technical discovery independently, distinguish stated problems from underlying needs, and redirect when the requested solution is not appropriate.
 * Deliver a working, useful system early in each engagement and iterate toward production hardening with appropriate ownership and documentation.
 * Design task-specific evaluations before building, define correctness with client subject matter experts, and hold systems to those evaluations before release.
 * Build retrieval and grounding systems over client data, including structured, unstructured, and graph-backed sources.
 * Integrate with client data platforms and business systems, including undocumented and legacy systems.
 * Instrument systems so accuracy, latency, cost, and drift are measured and hand off operations that client teams can run.
 * Build and deploy operator-facing application layers, including review and approval interfaces, agent and chat front ends, evaluation dashboards, and workflow tools.
 * Set realistic expectations with executives about AI capabilities and limitations.
 * Manage scope, timeline, and expectations against a defined statement of work and raise risk early.
 * Contribute reusable accelerators, evaluation harnesses, and reference architectures, and share implementation feedback with engineering and product teams.
 * Apply AI safety, data privacy, and governance controls appropriate to utility regulatory contexts.