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About This Role
*No sponsorship/C2C is available for this position*
Engineering and AI Specialist Reports To: Director of Technical Delivery Location: Remote FLSA: Exempt
Job Summary The AI Operations Specialist builds and runs the internal AI tooling, automations, and pilots that make AI a practical, everyday part of how Elevate Digital's Engineering team delivers. Reporting to the Director of Technical Delivery, this role turns the AI operations roadmap into working software: maintaining agent pipelines and internal tools, testing new models and tools against real workflows, and keeping engineers unblocked as they adopt them. This is an execution-focused role, not a standard-setting one. Direction, governance, and org-wide AI standards for Engineering remain owned by the Director of Technical Delivery; the AI Operations Specialist is the builder who makes that direction real, working alongside the Director on shared tooling projects rather than owning all of it independently.
Duties and Responsibilities Tooling & Automation
- Build, maintain, and extend the AI tooling and agent pipelines Engineering relies on — including coding-agent integrations, MCP servers, and internal bots or scripts — working alongside the Director of Technical Delivery on shared tooling priorities.
- Keep existing automations reliable as models, APIs, and internal workflows change, monitoring for breakage and patching quickly.
- Maintain a lightweight, current catalog of what tooling is live, what's experimental, and what's been retired.
Pilots & Evaluation
- Run scoped pilots applying AI to specific internal workflows — ticket triage, documentation, code review assistance, onboarding, and similar — and report results back to the Director of Technical Delivery.
- Hands-on evaluation of new AI models, tools, and vendors as they emerge: what they're good for, what they cost, and whether they fit Elevate's delivery workflows.
- Track basic usage, cost, and quality signals for AI tooling so adoption decisions rest on evidence rather than hype.
Enablement & Support
- Support engineers adopting AI tooling day to day — answering questions, unblocking issues, and sharing practical techniques that actually work.
- Produce lightweight documentation, guides, or walkthroughs so internal tools are usable by people other than the person who built them.
- Surface friction, bugs, and gaps in existing tooling and pipelines back to the Director of Technical Delivery as input to the roadmap.
Required Competencies and Skills
- Builder Mentality: Genuine hands-on drive to build and ship internal tools and automations — this role is defined by making things, not evaluating them from the sidelines.
- AI/LLM Practical Fluency: Working knowledge of agentic workflows, prompt engineering, MCP, and RAG-style patterns, gained through real building rather than credentials alone.
- Automation & Scripting: Comfortable writing scripts, integrations, and lightweight pipelines to connect tools and remove manual work.
- Judgment Under Ambiguity: Able to size up a new tool or pilot quickly, form a point of view, and know when a decision needs to go to the Director of Technical Delivery rather than being made unilaterally.
- Documentation Habit: Writes clear, lightweight documentation as a habit, not an afterthought, so tools and pilots are usable by others.
- Collaborative Execution: Works well executing someone else's roadmap and priorities on shared tooling projects, without needing to own the strategy to stay motivated.
Experience and Education
- Solid grounding in programming fundamentals — able to read, reason about, and debug the code and systems they build, not just accept AI-generated output at face value. This is not a "vibe coding" role.
- Demonstrated experience building automations, scripts, or AI-assisted tooling — through professional work, side projects, or hands-on experimentation. What you've built and can explain matters more than where you learned to build it.
- Practical, hands-on experience with agentic coding tools, AI coding assistants, or LLM-based pipelines, used deliberately rather than as a black box.
- Comfort operating in ambiguous, fast-changing technical territory where best practices are still being written.
- Self-taught and non-traditional paths are welcome, provided they come with real engineering fundamentals. Some formal computer science background is preferred, but demonstrated ability to understand and reason about the code and systems being built matters more than the credential itself.
- Bachelor's degree in Computer Science, Engineering, or a related field is a plus but not required given equivalent, demonstrable fundamentals.
Values Alignment The AI Operations Specialist embodies Elevate Digital's core pillars and the way we work:
- People Advantage: You build tools that make engineers faster and better at their jobs, not tools that quietly replace their judgment.
- Client Growth: You measure your work by whether it actually gets used and actually saves time — not by how sophisticated it looks.
- Services Transformation: You're energized by building in undefined territory, comfortable that today's tool may be rebuilt or replaced next quarter as the landscape shifts.
- Responsible by Default: You hold your own tooling to a high bar for reliability and security, especially anywhere it touches client work or sensitive data.
Travel Required This is a remote role with no regular travel expectation. Occasional participation in team gatherings or planning sessions may be requested.
Other Duties This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities required of the employee for this role. Duties, responsibilities, and activities may change at any time, with or without notice, as business needs evolve.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Elevate Digital, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Elevate Digital AI Hiring
Elevate Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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