Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
AI/ML Engineer (Mid-Level) - Hybrid (US Citizens/ Green Cards- Local to DMV only)
Northwest Washington, DC
Live, first seen by us 43 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: SALARIED_FT
From the employer’s posting
What you’ll work on
Full postingThe Mid-Level AI/ML Engineer is a hands-on technical contributor supporting the discovery, development, testing, deployment, and sustainment of DOL AI-enabled solutions.
Build AI/ML-enabled applications, APIs, and RAG pipelines
Implement agentic workflow features with audit logging and human-approval checkpoints
Support model fine-tuning, evaluation, release management, and production monitoring
Create automated unit, integration, and model-evaluation tests
Prepare technical documentation, runbooks, and ATO evidence
From the employer’s posting
The Mid-Level AI/ML Engineer is a hands-on technical contributor supporting the discovery, development, testing, deployment, and sustainment of DOL AI-enabled solutions. The position works closely with Senior AI/ML Engineers, data engineers, developers, business analysts, project managers, and DOL stakeholders to deliver secure, measurable, and maintainable AI capabilities.
Develop AI/ML-enabled applications, APIs, data-processing components, model integration layers, RAG pipelines
Implement agentic and workflow automation features, including approved MCP server or tool integrations, schema validation, controlled tool calls, audit logging, human-approval checkpoints
Support model development, fine-tuning, prompt/context engineering, evaluation, version control, release management, and production monitoring.
Create automated unit, integration, regression, data-quality, model-evaluation, and performance tests.
Prepare technical documentation, runbooks, test evidence, release notes, model documentation, and configuration records.
What you’ll bring
All qualificationsCore experience
- Bachelor’s degree in computer science, data science, software engineering, information systems, mathematics, statistics, or another relevant technical discipline.
- Experience with Python, SQL, REST APIs, Git, automated testing, and software-development practices.
- Experience or demonstrated proficiency with one or more of the following: ML frameworks, LLM APIs, RAG, embeddings, vector search, NLP, document intelligence, data pipelines, or model evaluation.
- Familiarity with AWS, Azure, Google Cloud, or comparable cloud-development environments.
- Ability to work effectively in a multidisciplinary agile team and communicate technical information clearly.
Preferred experience
- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, Hugging Face, MLflow, vector databases, containerization, or Kubernetes.
- Familiarity with Federal cybersecurity, accessibility, privacy, Responsible AI, FedRAMP, FISMA, NIST, or ATO practices.
- Experience supporting Government, public-sector, health, benefits, grants, inspections, case management, document-processing, or knowledge-management systems.
Qualification wording
Bachelor’s degree in computer science, data science, software engineering, information systems, mathematics, statistics, or another relevant technical discipline.
Experience with Python, SQL, REST APIs, Git, automated testing, and software-development practices.
Experience or demonstrated proficiency with one or more of the following: ML frameworks, LLM APIs, RAG, embeddings, vector search, NLP, document intelligence, data pipelines, or model evaluation.
Familiarity with AWS, Azure, Google Cloud, or comparable cloud-development environments.
Ability to work effectively in a multidisciplinary agile team and communicate technical information clearly.
Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, Hugging Face, MLflow, vector databases, containerization, or Kubernetes.
Familiarity with Federal cybersecurity, accessibility, privacy, Responsible AI, FedRAMP, FISMA, NIST, or ATO practices.
Experience supporting Government, public-sector, health, benefits, grants, inspections, case management, document-processing, or knowledge-management systems.
Education & alternatives
Required Qualifications - Bachelor’s degree in computer science, data science, software engineering, information systems, mathematics, statistics, or another relevant technical discipline. - At least three years of overall relevant experience, including at least two years in AI/ML implementation, NLP, data science, generative AI, or AI-enabled software development.
About Swingtech
Swingtech delivers innovative Information Technology and Professional Support services to a diverse range of clients across the federal and intelligence communities.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 22, 202643 days ago
- Recorded sightings
- 34
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 18, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 43
Similar jobs usually come down between day 6 and day 69.
From 4,347 similar postings that came down. Coming down is not always a hire.
Swingtech is hiring
5 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Kubernetes
- MLflow
- Hugging Face
- GCP
Where the posting names them
- Implement agentic and workflow automation features, including approved MCP server or tool integrations, schema validation, controlled tool calls, audit logging, human-approval checkpoints, and safe failure handling. - Develop robust code in Python, SQL, and other required languages using secure software-development practices. - Create automated unit, integration, regression, data-quality, model-evaluation, and performance tests.
- At least three years of overall relevant experience, including at least two years in AI/ML implementation, NLP, data science, generative AI, or AI-enabled software development. - Experience with Python, SQL, REST APIs, Git, automated testing, and software-development practices. - Experience or demonstrated proficiency with one or more of the following: ML frameworks, LLM APIs, RAG, embeddings, vector search, NLP, document intelligence, data pipelines, or model evaluation.
- Experience or demonstrated proficiency with one or more of the following: ML frameworks, LLM APIs, RAG, embeddings, vector search, NLP, document intelligence, data pipelines, or model evaluation. - Familiarity with AWS, Azure, Google Cloud, or comparable cloud-development environments. - Ability to work effectively in a multidisciplinary agile team and communicate technical information clearly.
Preferred Qualifications - Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, Hugging Face, MLflow, vector databases, containerization, or Kubernetes. - Familiarity with Federal cybersecurity, accessibility, privacy, Responsible AI, FedRAMP, FISMA, NIST, or ATO practices.
Your next step
Complete your application on ats.rippling.com. The employer’s form will show what is required.
Applying through Rippling
- “The résumé will be parsed to fill in the application details”: check what it filled before you send.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
About Swingtech
Swingtech delivers innovative Information Technology and Professional Support services to a diverse range of clients across the federal and intelligence communities. With over 15 years of trusted experience as a systems integrator, we apply agile methodologies and deep industry insight to help our customers achieve greater efficiency, compliance, and cost savings. At Swingtech, we’re committed to excellence and long-term success for our clients and our team.
AI/ML Engineer (Mid)
Position Summary
The Mid-Level AI/ML Engineer is a hands-on technical contributor supporting the discovery, development, testing, deployment, and sustainment of DOL AI-enabled solutions. The position works closely with Senior AI/ML Engineers, data engineers, developers, business analysts, project managers, and DOL stakeholders to deliver secure, measurable, and maintainable AI capabilities.
Essential Duties
- Develop AI/ML-enabled applications, APIs, data-processing components, model integration layers, RAG pipelines, retrieval services, workflow automations, and technical prototypes.
- Support model development, fine-tuning, prompt/context engineering, evaluation, version control, release management, and production monitoring.
- Build RAG capabilities using approved document ingestion, parsing, metadata extraction, chunking, embeddings, vector indexing, hybrid retrieval, reranking, citation validation, and knowledge-base refresh processes.
- Implement agentic and workflow automation features, including approved MCP server or tool integrations, schema validation, controlled tool calls, audit logging, human-approval checkpoints, and safe failure handling.
- Develop robust code in Python, SQL, and other required languages using secure software-development practices.
- Create automated unit, integration, regression, data-quality, model-evaluation, and performance tests.
- Support AI evaluation for model accuracy, relevance, hallucination, grounding, bias/fairness, explainability, safety, security, and user acceptance.
- Participate in CI/CD, DevSecOps, MLOps, code-review, security-scanning, documentation, and source-control processes.
- Prepare technical documentation, runbooks, test evidence, release notes, model documentation, and configuration records.
- Support privacy, security, Responsible AI, accessibility, and ATO evidence-development activities.
- Support operational monitoring, incident response, defect triage, corrective actions, and knowledge transfer.
- Participate in agile ceremonies, demonstrations, backlog refinement, sprint planning, and retrospective improvement activities.
Required Qualifications
- Bachelor’s degree in computer science, data science, software engineering, information systems, mathematics, statistics, or another relevant technical discipline.
- At least three years of overall relevant experience, including at least two years in AI/ML implementation, NLP, data science, generative AI, or AI-enabled software development.
- Experience with Python, SQL, REST APIs, Git, automated testing, and software-development practices.
- Experience or demonstrated proficiency with one or more of the following: ML frameworks, LLM APIs, RAG, embeddings, vector search, NLP, document intelligence, data pipelines, or model evaluation.
- Familiarity with AWS, Azure, Google Cloud, or comparable cloud-development environments.
- Ability to work effectively in a multidisciplinary agile team and communicate technical information clearly.
- Must be willing to work 3 days onsite at customer site in Washington, DC.
Preferred Qualifications
- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, Hugging Face, MLflow, vector databases, containerization, or Kubernetes.
- Familiarity with Federal cybersecurity, accessibility, privacy, Responsible AI, FedRAMP, FISMA, NIST, or ATO practices.
- Experience supporting Government, public-sector, health, benefits, grants, inspections, case management, document-processing, or knowledge-management systems.
Summary of Benefits
- 15 PTO days
- 11 paid holidays
- Medical Insurance with – 3 options (HSA with $600 Employer Contribution).
- Dental Insurance with no age limit orthodonture.
- Vision Insurance through EyeMed in and out of network coverage.
- Short Term and Long-Term Disability coverage with 100% premium support,
- Life insurance and AD&D with 100% premium support
- Supplemental Life Insurance
- Critical Care and Accident Insurance availability
- Pet Insurance through Nationwide
- Employee Assistance Program
- 401k with enrollment from day one. 4% deferral by company.
- $1500 Annual Training Budget
- $1500 Referral bonus
- Eligibility for annual merit and discretionary bonus
- Flexible work arrangements
Equal Opportunity Employer Minority/Female/Veterans/Disabled
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Northwest Washington, DC
- Support model development, fine-tuning, prompt/context engineering, evaluation, version control, release management, and production monitoring. - Build RAG capabilities using approved document ingestion, parsing, metadata extraction, chunking, embeddings, vector indexing, hybrid retrieval, reranking, citation validation, and knowledge-base refresh processes. - Implement agentic and workflow automation features, including approved MCP server or tool integrations, schema validation, controlled tool calls, audit logging, human-approval checkpoints, and safe failure handling.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.