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About This Role
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.
Swingtech Consulting is seeking a Business Analyst to support a newly awarded federal AI/ML solution delivery program. The Business Analyst will work directly with Government product owners, mission stakeholders, subject-matter experts, end users, data owners, and AI engineering teams to translate complex operational needs into clear, testable, and actionable solution requirements.
This role supports the full AI solution lifecycle, from use-case discovery and workflow analysis through backlog development, prototyping, user validation, production release, and continuous improvement. The successful candidate will be comfortable working in an Agile environment where requirements evolve as the team learns more about the mission problem, available data, technical feasibility, user needs, and AI-related risks.
Key Responsibilities
- Partner with Government product owners, mission stakeholders, subject-matter experts, and end users to understand operational objectives, pain points, workflows, policies, and desired outcomes.
- Facilitate discovery sessions, stakeholder interviews, requirements workshops, workflow reviews, and solution demonstrations.
- Document current-state and future-state business processes, decision points, user interactions, data needs, constraints, and dependencies.
- Translate agency priorities and mission needs into clearly defined epics, features, user stories, backlog items, and acceptance criteria.
- Maintain requirements and backlog traceability from the original business need through design, development, testing, user acceptance, release, and follow-up improvements.
- Work with AI/ML engineers, data engineers, developers, architects, security specialists, privacy officials, accessibility personnel, and program leadership to clarify requirements and resolve ambiguities.
- Help the team determine whether a mission problem is appropriate for AI, conventional automation, process improvement, structured search, analytics, or another technical approach.
- Identify the business rules, source data, user roles, permissions, exception paths, human-review requirements, and operational controls required by each use case.
- Support backlog refinement, sprint planning, sprint reviews, demonstrations, retrospectives, and other Agile delivery activities.
- Define measurable business outcomes and support development of success criteria for prototypes, pilots, and production releases.
- Assist technical teams in developing representative use cases, scenarios, test conditions, expected outcomes, and edge cases.
- Coordinate user acceptance testing, collect structured stakeholder feedback, document findings, and translate feedback into prioritized backlog actions.
- Support documentation of functional requirements, business-process models, use-case descriptions, user journeys, data flows, decision logic, and operating procedures.
- Identify and track business dependencies, assumptions, risks, issues, and decisions requiring Government direction.
- Support change management, stakeholder communications, user adoption, training materials, and knowledge transfer.
- Ensure requirements consider applicable security, privacy, accessibility, records-management, and Responsible AI expectations.
- Prepare clear, concise, and accessible documentation and presentation materials for both technical and nontechnical audiences.
The Government requires Agile ceremonies and actionable backlogs, scope and dependency tracking, coordinated project delivery, and AI/ML solution discovery and design.
Qualifications
- Bachelor’s degree in Business Administration, Information Systems, Computer Science, Data Analytics, Engineering, or a related discipline.
- Professional experience performing business analysis, requirements analysis, product analysis, or functional analysis for software, data, analytics, automation, or digital-transformation initiatives.
- Demonstrated experience eliciting, analyzing, documenting, validating, and managing business and functional requirements.
- Experience converting stakeholder needs into epics, features, user stories, acceptance criteria, process flows, and prioritized backlog items.
- Experience supporting Agile software-development teams and participating in backlog refinement, sprint planning, reviews, demonstrations, and retrospectives.
- Experience facilitating discussions with business users, product owners, technical teams, and senior stakeholders.
- Ability to investigate ambiguous mission problems, ask effective follow-up questions, and organize incomplete or conflicting information into actionable requirements.
- Ability to distinguish the underlying business problem from a stakeholder’s initially requested technology or solution.
- Experience supporting user acceptance testing, test scenarios, defect clarification, feedback collection, and release-readiness activities.
- Strong written and verbal communication skills, including the ability to explain complex workflows and technical concepts to nontechnical audiences.
- Experience with requirements and collaboration tools such as Jira, Confluence, Azure DevOps, Microsoft Visio, Lucidchart, or comparable platforms.
- Ability to create clear, accessible, and well-structured documentation.
- Ability to complete the required federal suitability, background-investigation, and onboarding process.
Preferred Qualifications
- Experience supporting AI, machine learning, Generative AI, LLM, RAG, intelligent document processing, predictive analytics, or workflow-automation initiatives.
- Understanding of AI concepts such as model outputs, confidence thresholds, false positives and false negatives, human-in-the-loop review, model evaluation, hallucinations, grounding, and Responsible AI.
- Experience working with data owners and engineers to document data sources, definitions, quality concerns, access restrictions, lineage, retention requirements, and integration dependencies.
- Experience supporting federal agencies or other regulated environments.
- Familiarity with federal cybersecurity, privacy, accessibility, records-management, and authorization processes.
- Familiarity with Section 508 and WCAG accessibility requirements. The PWS requires ICT deliverables, documents, training materials, software, and related content to meet applicable accessibility standards.
- Experience developing process maps, business-rule catalogs, requirements traceability matrices, decision tables, use-case inventories, user journeys, or data-flow diagrams.
- Relevant certification such as Certified Business Analysis Professional, Professional in Business Analysis, PMI Agile Certified Practitioner, Certified Scrum Product Owner, or equivalent.
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
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Swingtech Consulting Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Swingtech Consulting Inc. AI Hiring
Swingtech Consulting Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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