Applied AI Engineer - Information Intelligence & Automation (mid) (Falls Church, VA; St. Louis, MO)

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OtherApplied Ai Engineer

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Overview

Applied AI Engineer - Information Intelligence & Automation (mid) (Falls Church; St.

Technical Tools

OtherApplied Ai Engineer

Responsibilities

~3 min read

Build Applied AI Solutions

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Design and develop AI-powered applications and services using commercial and open-source foundation models.
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Work with models available through Amazon Bedrock, evaluating and selecting models based on capability, reliability, cost, latency, and operational requirements.
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Project token costs, submit token budget estimates and expected output values for prior approval, and present expected ROI for each project.
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Build production workflows incorporating LLMs, retrieval, structured outputs, APIs, and other AI capabilities.
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Use grounding, citations, retrieval, structured generation, and appropriate refusal or uncertainty behaviors to improve reliability and reduce hallucination.
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Work with Data Team and Customer Success colleagues to understand business and user needs into practical AI solutions.
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Clearly communicate model capabilities, limitations, risks, and expected performance to technical and nontechnical staff and clients.

Build Data Pipelines

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Build data ingestion and transformation pipelines for structured and unstructured sources, including databases, web content, PDFs, documents, and data integrated with Tesla’s GovShare platform.
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Automate data collection, cleaning, transformation, extraction, and analysis.
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Develop solutions for document processing, including OCR, layout-aware parsing, table extraction, and other techniques where appropriate.
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Design systems that maintain data quality, traceability, and provenance throughout the AI pipeline.

Own AI Evaluation & Reliability

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Own the evaluation harness for the AI features you build.
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Develop representative evaluation datasets and test cases for AI applications.
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Define measurable criteria for accuracy, relevance, grounding, hallucination, refusal behavior, and other task-specific requirements.
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Establish regression testing so that changes to prompts, models, retrieval systems, or code can be evaluated before implementation.
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Analyze failures and use evaluation results to improve prompts, models, retrieval strategies, data, and system architecture.
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Monitor production performance using AWS and Splunk, and identify opportunities to improve quality, cost, and latency.

Identify & Automate Opportunities

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Analyze existing workflows and identify opportunities where AI or automation can reduce repetitive work, improve decision-making, or increase efficiency.
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Work with departments across Tesla Government to understand their workflows and develop appropriate automation solutions.
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Prototype, evaluate, and implement approved automation opportunities.
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Measure the impact of new solutions, including time savings, improved accuracy, increased adoption, or other relevant business outcomes.
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Explore emerging AI technologies and determine where they can provide practical value rather than adopting technology for its own sake.

Collaborate Across Tesla

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Translate technical concepts and AI limitations for nontechnical audiences.
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Help staff and clients understand both the capabilities and limitations of AI-enabled tools.
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Contribute technical expertise to client engagements when appropriate.
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Participate in business development activities where technical expertise can help communicate the value and feasibility of AI solutions.

Delivering production-ready AI and automation solutions that solve meaningful government problems.
Time savings for the Data Team and other Tesla departments gained through new AI-based tools
Building AI systems whose performance can be measured in time savings, cost avoidance, user adoption, or other KPIs you establish with your supervisor.
Implementing citation, grounding, and refusal patterns so the model says "I do not know" instead of hallucinating
Improve the speed, cost, accuracy, or usability of existing workflows.
Helping teams across Tesla Government identify practical opportunities to use AI.
Clearly communicating what AI can and cannot do.
Demonstrating a measurable improvement in a workflow or product in Tesla within the first 90 days.

Requirements

~1 min read

Bachelor's degree in computer science, data science, engineering, statistics, or a related technical field, or equivalent professional experience.
5+ years of professional software, data, ML, or AI engineering experience, with demonstrated experience building and deploying technical solutions.
Professional experience developing applications using Python or another modern programming language.
Experience working with at least one commercial or open-source LLM and its API.
Experience building AI-powered applications using techniques such as retrieval, structured outputs, tool use, prompting, or grounding.
Experience working with APIs, data pipelines, or cloud-based services.
Experience containerizing and deploying applications using Docker and AWS ECR/ECS.
Demonstrated ability to troubleshoot complex technical problems and work independently when requirements or solutions are not fully defined.
Strong written and verbal communication skills, including the ability to explain technical concepts to nontechnical stakeholders.
Strong attention to detail and an ability to critically evaluate AI-generated outputs rather than assuming they are correct.

Experience building production RAG or LLM-based applications.
Experience designing or implementing AI evaluation frameworks, benchmarks, or regression testing.
Experience with Amazon Bedrock.
Experience with Splunk.
Experience with PDF/OCR pipelines, document parsing, layout-aware extraction, or table extraction.
Experience with vector databases, embeddings, search, or information retrieval.
Experience optimizing AI systems for cost, latency, scalability, and reliability.
Experience working with government, defense, or intelligence agencies.
Experience with data analytics, predictive modeling, or user behavior analysis.
Experience taking an AI prototype from experimentation through production deployment.

Resume
Cover Letter

Compensation: The expected initial salary for this role is $125,000 - $175,000 annually. Actual compensation may vary based on factors including, but not limited to, job-related knowledge and skills, education, experience, certifications, geographic location, and organizational needs. Individual compensation will be determined on a case-by-case basis.

In addition to base salary, Tesla Government offers a comprehensive benefits package, which includes medical, dental, and vision insurance, retirement savings options, paid time off, paid holidays, and professional development opportunities.

Tesla Government Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws.

This is a full time position

Visit Careers at Tesla Government