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About This Role
At Mitratech, we are a team of innovators focused on building world\-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close\-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading\-edge technologies available.
For over 35 years, the experts at Mitratech have been focused on solving the complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries.
As we continue to grow, we're always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Mitratech a truly exceptional place to work!
Job Overview
We're hiring a Senior AI Engineer to design, build, and operate intelligent agent systems end\-to\-end on our AWS\-native GenAI platform. You'll own major agent services from design through stable operations, design agent workflows and RAG architectures for team\-level use, define the testing and reliability practices for your areas, and mentor across the team. Agents are the headline, but this is a full\-stack platform role: you ship the FastAPI service, the Terraform, the MCP server, and the eval harness behind the agent — and you set the bar for how they're built.
Essential Duties \& Responsibilities:
- Design and own LLM\-powered agents end\-to\-end for internal tooling, customer\-facing features, and workflow automation — built on Bedrock with LangChain/LangGraph and the langchain\-aws/\-anthropic/\-core stack
- Design agent workflows and RAG architectures for team\-level use; influence team design beyond your own services
- Build and operate MCP servers that expose company data and services as structured tools for AI models
- Define the testing, reliability, and operational practices for your areas; own delivery and on\-call quality for major services
- Stand up evaluation pipelines (Langfuse \+ eval harnesses) and prompt\-versioning practices, and use them to drive reliability and cost/quality improvements
- Lead design reviews, mentor across the team, and drive improvements in engineering practice
- Collaborate with product, data, and platform teams to scope and deliver agent\-based solutions, applying platform patterns and governance hooks correctly
Requirements \& Skills:
- 5\+ years of software engineering experience, with multiple years building and operating LLM or AI agent systems in production
- Expert\-level Python and service development (async, FastAPI, Pydantic v2\), with a track record of owning services end\-to\-end
- Deep GenAI/LLM application experience — designing RAG architectures and agent workflows, ideally with Claude via Amazon Bedrock or the Anthropic API
- Strong AWS engineering (Bedrock, Lambda, ECS, API Gateway, and the surrounding services)
- Hands\-on agent tooling: LangChain/LangGraph and MCP (building and operating tool/resource servers)
- Terraform / IaC and containerized delivery through CI/CD
- Strong data\-layer experience (Redis, Snowflake, DynamoDB, Postgres/Aurora) via async SQLAlchemy
- Identity and app\-security fundamentals (JWT/OAuth, secrets management)
- Production observability and LLM evaluation experience (tracing, evals, latency/cost/quality metrics)
- Demonstrated ability to mentor engineers, lead design reviews, and set engineering practices
Nice to have:
- Data warehouse engineering (Snowflake)
- NoSQL / document databases (MongoDB)
- Frontend / web UI development (React 19, TypeScript) — used in the platform but not expected for this role
- Responsible AI / governance experience
We will disclose intended pay ranges in our job ads for US\-based opportunities – This role can be performed 100% remote anywhere in the US. Anticipated Pay Range: $170K– $190K Annually USD
Total compensation includes US employee benefits and annual bonus eligibility.
Benefits we offer:
- Health, Dental \& Vision Insurance \*
- 401 (k) \+ Employer Match \*
- Open PTO \+ 11 Paid Holidays \+ 4 Annual Paid Global Wellness Days Off
- STD, LTD \& Group Life Insurance
- Paid Parental Leave
- Pet Insurance
- FSA \& HSA Options
- Employee Assistance Program
Perks we offer:
- Remote Work
- Career Advancement \& Professional Development Opportunities
- Employee Recognition
- LinkedIn Learning Platform
*Mitratech is proud to be an EEOE, M/F/D/V, and we are committed to diversity both in practice and spirit at the corporate level. Mitratech participates in the Electronic Employment Verification Program. E\-Verify is an Internet\-based system that compares information from an employee's I\-9 to data from the U.S. Department of Homeland Security and Social Security Administration Records. To learn more, visit:* *everify.com*
*We are an equal\-opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability, or veteran status.*
Salary Context
This $170K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Mitratech, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $170K to $190K.
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.
Mitratech AI Hiring
Mitratech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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