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About This Role
Description: Job Title: AI Engineer – Agentic Orchestration (Senior)
Place of Performance: DC-Metro Area Preferred
Mandatory Requirements: Secret clearance
Experience Level: 6-8+ years
On-Site, hybrid, remote: Remote (Occasional customer on-site meetings)
Travel: Minimal / As Required
About JFL Consulting: With more than 20 years of securing some of the U.S. Department of Defense and the Intelligence Community’s most critical networks, JFL Consulting, LLC provides advanced network security solutions to a range of US Government and US commercial clients. Our cybersecurity operators are experts at assessing and defending mission-critical data and the networks that facilitate their operation. We are focused on delivering advanced products and industry best practices that meet each customer’s unique requirements. Visit www.jflconsulting.com
What We Offer:
- Salary: $160k - $190k
- 100% employer-paid medical, dental, and vision premiums for employees and dependents
- Flexible Spending Accounts (healthcare, dependent care, and commuter)
- Life insurance, short-term disability and long-term disability
- 401(k) with immediate vesting of company contribution
- Generous PTO policy (15 vacation, 5 sick, 2 personal days, 11 holidays)
- We support your growth through certification reimbursement, dedicated professional development funding, and company-provided access to online learning platforms
Position Overview
The AI Engineer – Agentic Orchestration is a senior back-end engineer supporting a pilot program that is re-platforming a capabilities onto an enterprise AI platform to deliver predictive, decision-grade decision making capabilities to the customer. The primary responsibility for this role is architecting, building, and maintaining the production API integrations and data-pipeline components that flow data from disparate sources into the program’s Common Operating Picture (COP), and orchestrating the agentic AI workflows that operate on that data. The engineer implements and versions robust interface contracts, builds high-throughput pipeline components that keep the COP reliable and current, writes rigorous integration and contract tests, tunes performance and reliability under heavy operational load, and authors clear technical documentation for long-term maintainability.
Key Responsibilities
API Integration & Data Pipelines
- Architect, build, and maintain production API integrations that ingest data from disparate operational, medical, and intelligence sources into the Common Operating Picture (COP).
- Design, implement, and version robust interface contracts (REST/GraphQL, OpenAPI specifications) that external and internal producers can reliably build against.
- Build and operate high-throughput, event-driven data-pipeline components (e.g., Apache Kafka) that keep the COP current, complete, and resilient to upstream failures.
- Integrate data products, ontologies, and pipelines within the platform environment (e.g., Palantir Foundry) and cloud-hosted services in AWS GovCloud.
- Implement enterprise authentication and authorization patterns (e.g., OAuth 2.0 / OIDC, SAML, PKI/CAC, attribute- and role-based access control) across integrated services.
Agentic Orchestration & AI Workflows
- Design and implement agentic AI workflows that chain models, tools, and data services to move data from after-the-fact reporting toward predictive, decision-grade warning.
- Build and maintain the orchestration layer (tool/function calling, prompt and workflow versioning, guardrails, retrieval, and evaluation harnesses) that exposes COP data to agents safely and repeatably.
- Instrument agent behavior with logging, tracing, and evaluation metrics so outputs are explainable, testable, and auditable by program stakeholders.
- Develop using open standards and avoid proprietary lock-in, consistent with Government data-rights requirements covering custom code, schemas, ontologies, prompts, and agentic workflows.
Quality, Testing & Documentation
- Write rigorous integration and contract tests; maintain automated CI/CD pipelines that validate interface compatibility on every change.
- Author and maintain clear technical documentation (interface specifications, data-flow diagrams, runbooks, and architecture decision records) for long-term maintainability and Government transition.
- Participate in design and code reviews; enforce coding standards and secure development practices.
Performance, Reliability & Security
- Tune system performance, throughput, and reliability under heavy operational load; identify and remove bottlenecks in ingestion, transformation, and serving paths.
- Build in observability (metrics, logs, alerts) and contribute to incident triage and root-cause analysis for integration and pipeline failures.
- Engineer to the program’s security posture, including NIST SP 800-53 Rev. 5 Moderate-High / IL5–IL6 requirements and CUI safeguarding under NIST SP 800-171 / DFARS 252.204-7012.
Collaboration & Delivery
- Work as an embedded member of the prime contractor’s integration team; coordinate closely with front-end, platform, data, and Government stakeholders.
- Deliver against fixed program milestones (Minimum Viable Product and Final Operational Capability) and report progress, risks, and technical dependencies to program leadership.
- Translate operational user needs from Combatant Command and medical stakeholders into integration and workflow requirements.
Requirements: Qualifications & Requirements
Mandatory Requirements
- Citizenship: U.S. Citizenship required.
- Clearance: Active Secret clearance.
Education
- Education: Bachelor’s degree in Computer Science or a related engineering field; or equivalent relevant experience.
Experience:
- 8+ years of professional back-end or API integration engineering experience.
- Demonstrated experience designing, versioning, and operating production APIs and data pipelines that other teams depend on.
- Experience building or integrating LLM-based or agentic AI workflows (tool calling, orchestration frameworks, retrieval, evaluation) in a production or prototype setting.
- Experience delivering software on Department of Defense, Intelligence Community, or other federal programs in cloud-hosted, security-controlled environments is strongly preferred.
Technical Skills & Competencies
- Expert-level Python.
- Deep experience with REST and GraphQL API design and OpenAPI specifications, including interface versioning and backward-compatibility strategies.
- Hands-on experience with Apache Kafka or comparable streaming/event platforms.
- Experience with Palantir Foundry (pipelines, ontology, data integration) is highly desired.
- Experience deploying and operating services in AWS GovCloud (or comparable IL5/IL6 cloud environments).
- Strong understanding of enterprise authentication and authorization patterns (OAuth 2.0 / OIDC, SAML, PKI/CAC, RBAC/ABAC).
- Familiarity with agentic AI tooling and patterns (e.g., LangGraph, LangChain, Semantic Kernel, or custom orchestration; function/tool calling; RAG; prompt and workflow versioning; model evaluation).
- Proficiency with integration and contract testing, CI/CD, containers, and infrastructure-as-code.
- Working knowledge of secure software development practices and NIST SP 800-53 / 800-171 controls as they apply to application and data-pipeline design.
Communication & Stakeholder Engagement
- Excellent written and verbal communication skills, including the ability to author precise technical specifications and documentation.
- Ability to explain integration architectures, data flows, and agent behavior clearly to engineers, program managers, and Government stakeholders.
- Self-directed and comfortable operating as a single senior engineer embedded in a partner team, managing competing priorities against fixed milestone dates.
Salary Context
This $160K-$190K range is above 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 JFL Consulting, 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 ($175K) sits 17% below the category median. Disclosed range: $160K to $190K.
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.
JFL Consulting AI Hiring
JFL Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Springfield, VA, US. Compensation range: $190K - $190K.
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.
Frequently Asked Questions
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