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About This Role
Company Description
Oteemo is an industry-leading technology consulting firm at the forefront of AI-driven, cloud native, enterprise DevSecOps transformation. We build intelligent, automated, secure systems for organizations tackling their toughest technical and business challenges and we're pushing the boundaries of what AI, generative AI, and agentic systems can do in production, not just in theory. Join us and you'll work alongside recognized experts on cutting-edge projects that blend cloud native architecture, extreme automation, and AI/ML at the core. We foster a dynamic, inclusive, and collaborative culture built on continuous learning, where your ideas shape real outcomes for our clients. If you're passionate about building what's next in AI and cloud technology and want to do it with a team that sets the standard rather than follows it, Oteemo is where you belong.
Job Description
Discovery & Solutioning: Lead discovery with mission owners, technical teams, and senior stakeholders to identify high-value AI and agentic use cases; translate strategic objectives into technical architectures, product roadmaps, implementation plans, prototypes, and engineering backlogs.
- Architecture & Platform Strategy: Design agentic workflows and solution architectures spanning LLMs, tools, APIs, data sources, orchestration, RAG/knowledge systems, human-in-the-loop controls, and evaluation; advise customers on AI platform choices, model selection, security, governance, deployment patterns, and build-vs-buy decisions.
- Consulting & Stakeholder Engagement: Serve as the technical counterpart to strategy-focused consulting partners, adding architectural depth and implementation realism to broader transformation efforts; facilitate architecture and product discussions with both executives and engineers; build trusted customer relationships and communicate complex technical decisions clearly to senior government stakeholders.
- Hands-On Delivery & Technical Leadership: Build prototypes or contribute hands-on when necessary to validate approaches and de-risk implementation; transition from solution architecture into technical leadership of the implementation team, helping engineers turn concepts into production capabilities.
Qualifications
Active Top Secret clearance with SCI eligibility/access required, with the ability and willingness to obtain a CI polygraph as required.
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 10+ years of technology, software, architecture, or technical consulting experience, including meaningful recent experience designing or implementing AI/ML or generative AI solutions.
- Deep understanding of modern agentic architectures beyond prompt engineering — orchestration, tool use, retrieval, context management, evaluation, observability, security, and production deployment.
- Hands-on engineering credibility: able to prototype, review designs and code, and challenge engineering assumptions; this is not primarily an individual-contributor developer role, but the candidate must be technically credible enough to ultimately lead a technical team.
Strong consulting skills — executive communication, structured problem solving, requirements discovery, workshop facilitation, relationship management, and the ability to turn ambiguous mission needs into actionable technical decisions.
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-Interim or Active Secret Clearance a must!
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.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Oteemo, Inc AI Hiring
Oteemo, Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Maryland City, MD, US, Remote, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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