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About This Role
Overview:
LMI is seeking a Software Developer, Applications/AI Automation Engineer to build automation, AI-enabled applications, prototypes, integrations, APIs, and production-ready software components for Department of Veterans Affairs (VA) modernization initiatives. The role spans no-code, low-code, and full-code delivery and favors approved reusable patterns before custom development. This position follows a hybrid work model, with an expectation of approximately 25% onsite presence at LMI’s Tysons headquarters or Washington, DC.
This is a builder role for an engineer who can move from rough problem statements to working technical increments quickly without sacrificing maintainability. The engineer should be comfortable deciding when configuration or low-code is sufficient, when custom code is justified, and how to evolve an early prototype into a supportable enterprise solution.
The successful candidate will bring modern software-engineering discipline to automation and AI-enabled work. That includes secure coding, testability, version control, API design, logging, documentation, code review, and careful handling of prompts, model integrations, data flows, and other components that can introduce new failure modes.
The engineer should take ownership of the full technical lifecycle, not just initial build tasks. Strong candidates will be curious about how a solution behaves with real data and users, responsive to defects and feedback, and disciplined about leaving behind code, configuration, documentation, and tests that another engineer can understand.
The ideal candidate is a pragmatic builder who can move quickly from validated workflow needs to testable solutions, troubleshoot across system boundaries, and package reusable code and configuration for future use.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.
Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value. Responsibilities:
- Build workflow automations, lightweight applications, prototypes, integrations, and production-quality components.
- Select the simplest approved implementation approach across no-code, low-code, and full-code options.
- Develop and troubleshoot APIs, scripts, services, prompts, agents, automation logic, and application components.
- Support technical feasibility testing, prototype evaluation, pilot readiness, implementation, and handoff.
- Apply secure coding, version control, testing, code review, documentation, and configuration-management practices.
- Partner with HCD/UX, data, architecture, security, platform, QA, and business stakeholders to fit real workflows.
- Package reusable code, prompts, configurations, integration patterns, and technical guidance.
- Resolve issues that cross application, data, integration, automation, or platform boundaries.
- Translate validated user and workflow needs into technical designs, implementation tasks, acceptance criteria, and working software across low-code, automation, API, and full-code approaches.
- Implement defensive error handling, logging, configuration management, secrets handling, automated tests, and observabilityappropriate tothe maturity and risk of each solution.
- Evaluate and integrate AI/LLM capabilities where justified, including prompt orchestration, retrieval, structured outputs, tool/API calls, evaluation logic, and guardrails within approved environments.
- Participateactively in peer review, technical troubleshooting, refactoring, and knowledge transfer so solutions can be maintained by the broader engineering team rather than a single developer.
- Use technical spikes and feasibility tests to reduce uncertainty around APIs, data access, model behavior, platform limits, integration constraints, performance, and security before committing to a larger build.
- Profile and improve reliability, latency, cost, maintainability, and user-facing behavior as solutions mature, using evidence from testing and operational feedback rather than relying on prototype assumptions.
Qualifications:
- Bachelor's degree in computer science, software engineering, information systems, engineering, or a related field; equivalent experience may be considered.
- 7+ years in application development, automation engineering, systems integration, prototyping, or related software delivery.
- Strong coding in Python, JavaScript/TypeScript, C#, Java, or comparable languages plus hands-on API and integration experience.
- Experience choosing among no-code, low-code, and full-code approaches based on complexity, security, maintainability, and enterprise constraints.
- Recommended certification: cloud developer, Microsoft Power Platform Developer, DevOps, or comparable software-engineering credential.
- Ability to satisfy VA personnel vetting and applicable security, privacy, records, and data-handling requirements.
- Demonstratedexperience delivering production-quality software or automation through a modern SDLC, including code review, branching, testing, release discipline, documentation, and defect resolution.
- Strong understanding of API design, authentication/authorization, data interchange, integration patterns, secure coding, and troubleshooting across application and service boundaries.
- Ability to evaluate tradeoffs among custom development, configuration, scripting, low-code, and managed services based on complexity, security, supportability, performance, and lifecycle cost.
- Strong debugging ability across application code, APIs, data transformations, authentication, configuration, network/service dependencies, and third-party or managed services.
- Experience estimating engineering work, decomposing requirements into implementable increments, and communicating technical risk or tradeoffs clearly to non-engineering stakeholders.
Desired Qualifications* 9+ years delivering enterprise applications, automation, integrations, or AI-enabled solutions in federal or regulated environments.
- Experience with generative AI/LLMs, agentic workflows, Azure AI services, Power Platform, Azure Functions, Logic Apps, or comparable tooling.
- Experience with CI/CD, containers, infrastructure integration, observability, and production support.
- Additionalcloud AI, security, DevSecOps, or advanced developer certification is preferred.
- Hands-on experience evaluating or implementing generative-AI application patterns, retrieval-augmented generation, agent/tool workflows, prompt evaluation, or model-service integration is preferred.
- Microsoft Azure AI Engineer, Power Platform Developer, cloud developer, software engineering, orDevSecOpscertifications are valuable when supported by strong practical engineering experience.
- Experience with Azure Functions, Logic Apps, Microsoft 365/Power Platform integration, containerized services, or comparable enterprise automation technologies is helpful.
- A portfolio of reusable libraries, internal tools, open-source contributions, technical prototypes, or documented engineering patterns can strengthen a candidate's qualifications.
Target salary range: $140,000-$170,000 Disclaimer: The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.
#LI-SH1 Job Locations: US-VA-Tysons US-DC-Washington, DC
Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At LMI, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $140K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
LMI AI Hiring
LMI has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, Tysons, VA, US. Compensation range: $170K - $225K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
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