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About This Role
Engineer 3 - (Python, AI/ML, Kafka)
Clearance: TS/SCI - Polygraph required
Position ID: 20-22-003-SE3
Location: Columbia, Maryland
Description:
You will be part of a team responsible for delivering mission-critical infrastructure cognizance. You will be tasked with aggregating data sources to create a clear picture of what infrastructure was requested, what infrastructure was deployed, and proactively alerting on any discrepancies. The team’s application is used by subject matter experts to investigate, create, and iterate on best practices and deployment rules, and the team detects and surfaces cases where requested or provisioned infrastructure breaks those best practices and rules. Given the visibility and impact of this work, the team interacts with many stakeholders from across the organization and iterate as frequently as possible. You will drive end-to-end feature delivery from concept to production, transforming complex data into actionable insights and leveraging AI to accelerate analysis and solution design.
- Design, develop, and maintain full-stack applications (frontend and backend).
- Apply AI tools and techniques to accelerate analysis and prototype new solutions quickly.
- Develop ETL workflows to reconcile requested vs. realized infrastructure.
- Ensure scalable and reliable data ingestion and processing.
- Collaborate with full-stack engineers on integration and visualization of results.
Position Required Skills:
- Strong proficiency in Python.
- Demonstrated use of AI/ML tools to accelerate data analysis and engineering solution development.
- Experience with Kafka and streaming analytics.
- Working knowledge of relational databases.
- Exposure to adjacent domains such as dataflow, data science, or mission analytics is a plus.
Nice to Haves
- Hands-on experience with production CNO capabilities and operations.
- Experience analyzing cyber security data sets such as network traffic and OS events.
- Understanding of end-to-end SIGINT collection and analysis systems.
- Familiarity with ClickHouse.
- Proficiency with Git and GitLab.
- Working knowledge of Atlassian tools such as Jira and Confluence.
SE3 Qualifications:
Twelve (12) years of experience as a SE in programs and contracts of similar scope,
type and complexity is required. Demonstrated experience in planning and leading Systems Engineering efforts is required. Bachelor's degree in System Engineering, Computer Science, Information Systems, Engineering Science, Engineering Management, or related discipline from an accredited college or university is required. Four (4) years of additional SE experience may be substituted for a bachelor's degree.
Salary Range: $242k-$296k (Annually)
The range displayed above is a likely salary range for this position. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possible contractual requirements and could fall outside of this range.
Akina is a Woman Owned, Service Disabled, Veteran Owned, Small Business, looking for talented and ambitious individuals to join our team. We offer a generous compensation package that includes 24 days PTO accrued annually and 11 federal holidays. Our 401k is 100% vested on your start date and the company makes a direct contribution worth 10% of your salary. Akina covers 100% of healthcare costs for employees and 50% toward dependents. We offer educational assistance towards college classes and will cover costs associated with job related training and certifications.
Akina is committed to excellence and creating innovative and flexible solutions for our clients. We are a small company with an open ear to our employees' needs in order to attract and retain quality talent that enables our customer's mission.
We are an equal employment opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
www.akina-inc.com/careers
Salary Context
This $242K-$296K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Akina, 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 $212,160 based on 13,809 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($269K) sits 27% above the category median. Disclosed range: $242K to $296K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Akina, Inc. AI Hiring
Akina, Inc. has 6 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Columbia, MD, US, Fort Meade, MD, US, Annapolis Junction, MD, US. Compensation range: $207K - $296K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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