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About This Role
Company Description
Join Oteemo and become part of a transformation powerhouse where innovation meets impact. We're not just another consulting firm—we're architects of digital evolution, blending cutting\-edge technical expertise with human\-centered design principles to create solutions that resonate. Our work spans Infrastructure, Software Development, DevSecOps, Cybersecurity, Experience and Design, Organizational Change Management, and AI\-enabled solutions, but our approach is what truly sets us apart. We measure success through tangible business outcomes, not billable hours. We foster a culture of continuous learning where your ideas can thrive and technical excellence is celebrated. Our collaborative global team works across borders and time zones, tackling complex challenges for both Commercial Enterprise and Federal Defense clients with equal passion and precision. At Oteemo, you'll have the opportunity to work with emerging technologies and develop your skills alongside industry experts who are reshaping digital landscapes. If you're seeking a place where your technical prowess can drive meaningful change and where innovation isn't just encouraged—it's expected—Oteemo is your next career destination.
Job Description
We are seeking an experienced AI Engineer to join our team and lead the development of cutting\-edge artificial intelligence solutions. In this role, you will architect end\-to\-end AI systems, from proof of concept through production deployment, ensuring they meet performance targets while adhering to ethical AI practices. You will work with cross\-functional teams to translate business requirements into scalable AI solutions that deliver real impact.
Key Responsibilities
System Architecture \& Development
- Design and implement AI architectures including model selection, data pipelines, training infrastructure, and inference systems.
- Develop production\-ready AI models achieving \<100ms inference latency and optimal accuracy targets.
- Build and optimize machine learning models using deep learning, NLP, computer vision, and reinforcement learning techniques.
- Implement model optimization techniques including quantization, pruning, and knowledge distillation.
Production Deployment
- Deploy AI solutions across diverse platforms including REST APIs, gRPC, serverless, and edge devices.
- Establish CI/CD pipelines, automated testing, and model versioning systems.
- Implement A/B testing frameworks and monitoring dashboards for continuous improvement.
- Ensure 99\.9% uptime and reliability for production AI systems.
MLOps \& Infrastructure
- Design and maintain MLOps infrastructure using tools like MLflow, Kubeflow, or similar platforms.
- Implement model registries, feature stores, and experiment tracking systems.
- Optimize resource utilization and manage distributed training environments.
- Configure monitoring, alerting, and rollback procedures for production models.
Ethical AI \& Governance
- Implement bias detection and fairness metrics across all AI systems.
- Develop explainability tools and model documentation.
- Ensure compliance with data privacy regulations and governance frameworks.
- Conduct robustness testing and establish audit trails.
Collaboration \& Leadership
- Partner with data scientists, software engineers, and product teams to deliver integrated solutions.
- Mentor junior engineers and drive AI best practices across the organization.
- Communicate technical concepts to non\-technical stakeholders.
- Lead technical discussions and architectural reviews.
Qualifications
- Active DOD Secret Clearance
- 5\+ years of hands\-on experience building and deploying AI/ML systems in production.
- Proven track record of delivering AI solutions with measurable business impact.
- Experience with large\-scale data processing and distributed computing.
- Strong understanding of machine learning algorithms, neural networks, and deep learning architectures.
- Experience with model optimization and performance tuning.
- Knowledge of software engineering best practices and design patterns.
- Excellent problem\-solving and analytical skills.
- Strong communication and presentation abilities.
- Proficiency with AI/ML or data science tools (Python, Pandas, or similar).
- Experience using LLMs for workflow automation, data analysis, or summarization.
- Clear communication skills to explain AI\-driven findings to engineering, security, and compliance audiences.
- Hands\-on experience building, training, and deploying AI/ML models in production.
- Strong foundation in Machine Learning, Deep Learning, NLP, and Generative AI.
- Proficiency with LLM platforms: OpenAI, HuggingFace, Anthropic Claude, Cohere, Mistral, LLaMA.
- Experience building Agentic AI systems using LangGraph, AutoGen, CrewAI, or LangChain Agents.
- Hands\-on with RAG architectures using LangChain, LlamaIndex, and vector databases (FAISS, ChromaDB, Pinecone, Weaviate).
- Understanding of embeddings, vector similarity search, and semantic retrieval.
- Experience with Docker, Kubernetes, and containerized deployments.
- Hands\-on with CI/CD pipelines and version control (Git).
- Ability to explain complex AI concepts and data\-driven findings to technical and non\-technical audiences.
- Strong documentation skills to enable knowledge transfer and adoption.
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; Master's degree preferred.
Additional Information We Value:
- Drive: Passion and energy to implement quality technical solutions. Self\-motivation and intellectual curiosity
- Commitment to Quality: Passion to conceive and produce world\-class solutions that drive real\-world value for the customer
- Customer Focus: Consultative approach to solving problems for customers. Expectations management.
- Communication: Superior communication skills. Ability to clearly articulate problems, solutions, risks, rewards etc. (written and verbal)
- Technical Skills: Love for technology. You have to be inherently passionate about technology.
- Business Acumen: Technology ultimately is used to enable the business. We look for people who understand how the businesses can be enabled through their technical solutions
What we offer:
- Ability to make a noticeable difference for the organization and our customers
- Tremendous growth opportunity by becoming part of a rapidly growing organization. It’s not your tenure but what you can bring to the table that defines how your career will be shaped. You control your growth.
- Complex but interesting challenges to improve the depth and breadth of your technical and business skills. Our consultants are business technologists and understand how technology drives business.
- Competitive pay and benefits
Work authorization requirement: US Citizen.
*Oteemo is an equal employment and affirmative action employer. We evaluate qualified applicants on merit and business needs and not on race, color, religion, creed, gender, sexual orientation, national origin, ancestry, age, disability, genetic information, marital status, veteran status or any other factor protected by law. Oteemo complies with the law regarding reasonable accommodations for handicapped and disabled employees.*
*It has come to our attention that unauthorized individuals may be impersonating our company or recruiters using fake email addresses or social media profiles to contact job seekers. These messages may offer fake job opportunities or request sensitive information under false pretenses.*
*Please be advised:*
- *All official communications* *from our company will come from email addresses ending in @oteemo.com.*
- *We* *do not ask* *for personal information such as your Social Security number, bank account details, or payment of any kind during the recruitment process.*
- *If you receive an email or message that seems suspicious,* *do not respond**, click on any links, or provide any information.*
- *You can verify the legitimacy of any communication by contacting us directly through our official website or LinkedIn page.*
*We take these matters seriously and are committed to protecting the integrity of our recruitment process.*
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Oteemo, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Oteemo, Inc AI Hiring
Oteemo, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Manassas, VA, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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