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About This Role
Agentic AI \& LLM Applications Software Development Engineer, SeniorThe Opportunity:
To achieve an organization’s mission, leaders need strong team members who can build the next generation of agentic AI to transform how clients accelerate research, makes decisions, and ships products at scale. That is why we need you, an experienced Software Development Engineer who can operate at a system\-of\-systems level to support clients in advancing AI\-enabled systems within an R\&D environment.
As part of our team, you'll serve as a Software Development Engineer to the Advanced Research Projects Agency for Health (ARPA\-H). ARPA\-H has a small team that is building the next generation of agentic AI to transform how the agency accelerates research, makes decisions, and ships products at scale. The team will evolve ARPA\-H's production AI assistant into an ecosystem of autonomous, multi\-agent systems.
You'll serve as a Software Development Engineer at the application layer to design and build agentic workflows, build LLM integrations, support tool\-calling systems, and develop AI\-powered features that users interact with every day. Your focus will be on what runs on top of the platform: the agents, the orchestration, the prompts, the pipelines, and the product. Your attention to detail, flexibility, communication skills, understanding of the client's mission, and problem\-solving will enable the mission's success.
What You’ll Work On
- Support agentic AI systems and orchestration, LLM application development, features and products, observability and reliability, and engineering excellence
- Design and build core agentic workflows: multi\-step reasoning, planning, memory, and tool\-use across single and multi\-agent systems
- Implement and evolve A2A communication patterns at the application layer, enabling agents to collaborate and hand off tasks, and build and maintain the tool\-calling layer, including tool definitions, input and output schemas, error handling, retry logic, and result formatting
- Own the MCP client\-side integration, including how agents discover, invoke, and compose tools exposed via MCP servers
- Design multi\-agent workflows that are reliable, observable, and debuggable in production, not just in demos
- Own LLM orchestration at the application layer, including prompt construction, context management, model selection logic, and response parsing
- Build and maintain RAG features, including query formulation, result ranking, citation grounding, and hallucination mitigation; implement and iterate on prompt engineering patterns and system prompts that drive GRACE's quality and consistency across OpenAI GPT, Anthropic Claude, and Google Gemini
- Manage context window budgets and know when to truncate, summarize, or paginate, and build the logic that makes those decisions correctly
- Build evaluation pipelines for LLM quality, including grounding assessment, regression testing, safety checks, and A/B experimentation on prompt and model changes
- Stay sharp on token economics and write prompts and pipelines that are cost\-efficient without sacrificing output quality
- Translate ambiguous product requirements into clear technical designs and ship them fast, build new product capabilities end\-to\-end, including from backend application logic through to the API contract the frontend consumes, and rapidly prototype new agentic features, run experiments, collect data, and iterate based on real user behavior
- Collaborate closely with product, UX, applied science, and operations, write tests, handle edge cases, and make sure features degrade gracefully when upstream dependencies fail
- Instrument agentic workflows with tracing, logging, and metrics so failures are diagnosable and regressions are caught before users report them
- Define and monitor application\-level SLOs: tool call success rates, response quality, and latency from the user's perspective, build fallback and guardrail logic for AI services, including what happens when a model returns something unsafe, off\-topic, or structurally wrong, and work closely with the infra engineer to understand system\-level constraints and design application behavior that respects them
- Write production\-quality code: readable, tested, reviewed, and documented
- Communicate technical decisions clearly to both engineers and non\-engineers; no one should have to guess what you decided or why, participate actively in design reviews, and push back when something is over\-engineered or under\-specified
- Ensure strong privacy, security, and compliance in all application logic and data handling
Join us. The world can’t wait.
You have:
- 7\+ years of experience with software engineering, including building and operating production systems
- Experience in high\-velocity environments where you owned and shipped complex products end\-to\-end
- Experience with at least 2 backend languages, including Python
- Experience building and operating systems on major cloud platforms, such as AWS, GCP, or Azure
- Experience with containerization and working within CI/CD pipelines
- Knowledge of modern backend frameworks and async patterns
- Knowledge of algorithms, data structures, APIs, and software design patterns
- Bachelor's degree in Computer Science or Software Engineering
Nice if you Have:
- Experience building production systems on top of LLMs, including tool\-calling, RAG, multi\-step reasoning, and context management, and multi\-agent (A2A) architectures and orchestration frameworks in production, not just in prototypes
- Experience with MCP at the client and consumer layer and prompt engineering and LLM behavior across model families
- Experience building LLM evaluation and regression testing pipelines
- Experience in startup or early\-stage environment, including 0\-to\-1 product building, big tech building customer\-facing AI platforms or developer tools at scale, security\-conscious engineering, input validation, output sanitization, audit logging, and responsible AI guardrails
- Experience in healthcare, life sciences, or other regulated domains
- Knowledge of why Claude and GPT respond differently to the same prompt, how to design for it, and how agents discover and invoke tools via MCP
- Knowledge of token economics: cost\-per\-query awareness, context budget management, and prompt efficiency
- Ability to be comfortable with ambiguity and a high sense of urgency
- Ability to be a self\-starter, operate within a fast\-paced environment, multi\-task and handle multiple priorities
- Possession of excellent oral and written communication skills
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well\-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work\-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full\-time and part\-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract\-specific affordability and organizational requirements. The projected compensation range for this position is $86,800\.00 to $198,000\.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date.Identity Statement
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in\-person or virtual) is prohibited unless permission is explicitly provided.
Work Model
Our people\-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.
- Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
- Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
- Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.
Commitment to Non\-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.
Salary Context
This $86K-$198K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Booz Allen Hamilton, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 21% below the category median. Disclosed range: $86K to $198K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Booz Allen Hamilton AI Hiring
Booz Allen Hamilton has 20 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Research Engineer. Positions span Arlington, VA, US, San Diego, CA, US, Fort Meade, MD, US. Compensation range: $158K - $292K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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