AI Platform Engineer

Harley Ellis Devereaux•San Diego, CA

1d•Hybrid

About The Position

HED is hiring an AI Platform Engineer to develop, evaluate, and improve AI capabilities that support its people and practice. Working within the Data & AI team at a national integrated architecture and engineering firm, the engineer will build and optimize production AI solutions, evaluate large language models, improve AI-generated responses, and transform organizational knowledge into secure, accessible, and actionable intelligence. The role owns defined technical initiatives and collaborates with developers, data engineers, and business stakeholders, with opportunities to influence technical decisions and grow into broader platform leadership. The position includes developing model benchmarks and evaluation methods; optimizing retrieval-augmented generation, prompts, context management, and caching; delivering AI use cases; creating workflows and data models to capture employee expertise; applying data governance and responsible AI practices; and contributing to technical reviews, coding standards, testing, CI/CD, and code reviews. The engineer will also assess emerging generative AI and agentic technologies for practical use at HED. The role follows a hybrid work model, with the freedom to work from home and regular in-office days. The office is a professional, open-space environment designed for collaborative and independent work. Physical requirements include prolonged sitting and computer use, effective communication in writing and verbally, and visual acuity to perform responsibilities.

Requirements

Approximately 4–6+ years of experience in software engineering, machine learning engineering, data engineering, or a related technical discipline.
Hands-on experience developing, deploying, or supporting LLM applications in production.
Experience implementing AI evaluations, benchmarks, or performance testing.
Working knowledge of RAG, information retrieval, prompt engineering, and context optimization.
Proficiency in Python and SQL, Git, automated testing, and modern software development practices.
Experience with cloud applications, APIs, and data integration workflows.
Understanding of enterprise data governance, privacy, and security.
Ability to independently deliver complex technical assignments and communicate effectively with technical and nontechnical stakeholders.

Nice To Haves

Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related field, or equivalent practical experience.
Experience with Azure OpenAI, Amazon Bedrock, Anthropic, or comparable AI platforms.
Familiarity with Databricks, Delta Lake, Unity Catalog, MLflow, or similar platforms.
Experience with AI agents, automated workflows, experiment tracking, or multi-model systems.

Responsibilities

Develop and maintain model benchmarking, evaluation datasets, testing methods, and performance baselines across production AI applications.
Evaluate models, prompts, and configurations for response quality, reliability, cost, latency, and risk; document findings and recommend improvements.
Support preproduction validation of model, prompt, and application changes.
Develop and optimize retrieval-augmented generation (RAG), retrieval ranking, prompt structures, context management, and caching.
Measure answer quality, token usage, and operating costs and use results to guide improvements.
Collaborate with application developers to improve shared AI capabilities and user experiences.
Deliver defined AI use cases from requirements and design through implementation, testing, documentation, adoption, and improvement.
Help design workflows that capture and organize employee expertise, skills, and competencies for AI-powered applications.
Develop structured data models, skills taxonomies, and knowledge capture workflows; partner with data engineering to integrate governed datasets.
Work with business stakeholders to define requirements, success criteria, and practical solutions.
Apply privacy, security, data classification, consent, retention, and access-control requirements to AI solutions and datasets.
Support dataset quality, lineage, ownership, documentation, and auditability.
Contribute to architecture discussions, technical reviews, coding standards, automated testing, CI/CD, and code reviews.
Use AI-assisted development tools responsibly and share technical knowledge with colleagues.
Evaluate emerging generative AI and agentic technologies for practical application at HED.