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About This Role
Steel Point Solutions is an amazing SBA Certified (8a), HUBZone, Small Disadvantaged Business (SDB) and a Woman Owned Small Business (WOSB) company. Established in 2013 with a vision of offering world class, integrated business solutions for all levels of Government and commercial enterprises. We are represented by a team of talented and qualified professionals who know how essential efficient, cost\-effective integrated solutions are to your organization's success. Leveraging these resources, we strive daily to lead the industry in program management and service delivery.
Role Summary
The AI/ML Engineer is responsible for designing, developing, and deploying machine learning models and artificial intelligence solutions that drive Steel Point's data\-driven initiatives. This role involves creating algorithms, working with large datasets, and implementing AI/ML solutions to solve complex business problems. The AI/ML Engineer will collaborate with data scientists, software engineers, and other stakeholders to integrate and optimize AI/ML systems within existing infrastructure.
Key Roles \& Responsibilities
- Model Development: Design, develop, and train machine learning models, including supervised, unsupervised, and reinforcement learning algorithms.
- Data Processing: Prepare and preprocess large datasets for training and validation of AI/ML models, including data cleaning, feature engineering, and transformation.
- Algorithm Implementation: Implement and optimize AI/ML algorithms using industry\-standard libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit\-Learn).
- Deployment: Deploy AI/ML models into production environments, ensuring scalability, reliability, and performance.
- Performance Monitoring: Monitor and evaluate model performance, making necessary adjustments and improvements to enhance accuracy and efficiency.
- Collaboration: Work closely with data scientists, software engineers, and business stakeholders to understand requirements and integrate AI/ML solutions into applications and systems.
- Documentation: Create and maintain documentation for AI/ML models, including design, development, and deployment processes.
- Innovation: Stay updated with the latest advancements in AI/ML technologies and methodologies, applying new techniques to improve existing models and solutions.
- Ethics and Compliance: Ensure AI/ML solutions adhere to ethical guidelines and regulatory requirements, including fairness, transparency, and privacy.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
- 3\+ years of experience in AI/ML engineering, including hands\-on experience with model development and deployment.
- 3\+ years of experience with data processing and manipulation using tools such as Pandas, NumPy, and SQL.
- Proven experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras)
- Candidate Must Have an Active Top Secret SCI Poly Security Clearance.
Preferred Qualifications
- A Master's degree or PhD in a relevant field is preferred
- Certifications:
+ AI/ML\-related certifications (e.g., Google Cloud Certified \- Professional Machine Learning Engineer, AWS Certified Machine Learning \- Specialty) are preferred.
+ Certifications in data science or analytics (e.g., Certified Data Scientist) are a plus.
Skills and Competencies
- Machine Learning: Strong knowledge of machine learning algorithms, techniques, and best practices.
- Data Engineering: Proficiency in data processing, feature engineering, and working with large datasets.
- Technical Skills: Experience with AI/ML frameworks and libraries, and programming languages such as Python, R, or Java.
- Problem\-Solving: Strong analytical and problem\-solving skills to address complex AI/ML challenges and optimize model performance.
- Collaboration: Ability to work effectively with cross\-functional teams, including data scientists, engineers, and business stakeholders.
- Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non\-technical audiences.
- Ethical Awareness: Understanding of ethical considerations and regulations related to AI/ML technologies, including fairness and privacy.
Candidates from Historically Underutilized Business Zones (HUBZone) are strongly encouraged to apply. To determine whether you reside in a HUBZone, visit: https://maps.certify.sba.gov/hubzone/map.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Steel Point Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Steel Point Solutions AI Hiring
Steel Point Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Meade, MD, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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