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About This Role
Overview:
Location: Remote within USAEngagement Type: Contract (W2\)
Build AI That Actually Reaches the Clinic
Are you excited by the idea of building real, production AI systems that directly impact patient care? Trissential is partnering with a leading healthcare organization to hire AI/ML Engineers who thrive at the intersection of advanced AI, cloud engineering, and healthcare delivery. This is not an experimental or academic role. You’ll design, build, evaluate, and deploy production‑grade AI and agentic systems that clinicians and operational teams rely on every day.
What’s in It for You?* Real‑World Impact – Your AI solutions will be deployed into clinical workflows, directly improving patient care and healthcare operations
- Modern AI Stack – Work hands‑on with LLMs, agent architectures, RAG pipelines, and cloud‑native AI systems
- Healthcare‑Grade Engineering – Build solutions that meet HIPAA, PHI, and regulatory requirements
- Technical Ownership – Influence architecture, guardrails, evaluation frameworks, and deployment patterns
- Collaborative Environment – Partner closely with clinicians, product leaders, UX designers, and data teams
Your Role \& Responsibilities* Design and implement agentic AI systems, including LLM integrations, orchestration patterns, prompt engineering, and tool‑using agents
- Build and maintain production Python services, APIs, automation workflows, and data pipelines
- Develop RAG pipelines using embeddings, vector databases, and structured/unstructured healthcare data
- Implement evaluation frameworks and guardrails to address hallucinations, safety, PHI protection, and quality before production
- Deploy, monitor, and optimize AI/ML solutions in cloud environments using CI/CD pipelines
- Collaborate cross‑functionally to translate clinical and operational needs into scalable AI solutions
- Ensure solutions meet healthcare regulatory, quality, and risk mitigation standards
- Contribute to AI/ML best practices, MLOps/LLMOps methodologies, and continuous improvement
Skills \& Experience You Should Possess* 7\+ years of experience in software engineering, ML engineering, or AI engineering
- Strong Python experience in production environments (APIs, async workflows, testing)
- Hands‑on experience designing and deploying AI / LLM agent systems
- Experience with at least one agent framework such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or Google ADK
- Practical experience with RAG architectures, embeddings, and vector databases
- Experience deploying AI systems in Azure, AWS, or GCP
- Understanding of HIPAA / PHI handling, including de‑identification and secure AI workflows
- Strong communication skills and ability to explain complex AI concepts to non‑technical stakeholders
Bonus Points If You Have* Experience with Google Cloud or Azure in regulated environments
- MCP, A2A, or protocol‑driven AI architectures
- Experience with BigQuery, Firestore, Cloud SQL, or Dataflow
- MLOps or LLMOps experience in enterprise environments
- Infrastructure as Code (Terraform) and CI/CD (Azure DevOps Pipelines)
- Experience with healthcare informatics standards and clinical data models
Education \& Certifications You Need* Master’s degree in Engineering, Computer Science, Mathematics, Health Science, or related field with 1\+ year experience
OR
- Bachelor’s degree with 3\+ years of relevant experience
OR
- HS Diploma/GED with 7\+ years of experience
What We Offer
At Trissential, we believe great engineers deserve flexibility, transparency, and meaningful work.
- Competitive Compensation –$85–$95 per hour. Final compensation is determined based on skill alignment, years of experience, and fair, market‑based rates by geography
- Comprehensive Benefits for you and your dependents – Medical, dental, vision, free tele‑health, HSA with company contribution, life and disability insurance, and 401k with matching
- Paid Time Off – Offers paid time away from work
- High‑Impact Healthcare Work – Build AI systems used in real clinical environments
- Professional Growth – Work alongside senior AI engineers, clinicians, and healthcare leaders
This role is open only to candidates authorized to work in the United States.
Ready to Build AI That Matters?
If you’re excited to design and deploy AI systems that make a real difference in healthcare, we’d love to connect. Apply now and take the next step in your AI engineering career with Trissential.
Salary Context
This $176K-$197K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Expleo Group, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($187K) sits 5% above the category median. Disclosed range: $176K to $197K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Expleo Group AI Hiring
Expleo Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rochester, MN, US. Compensation range: $197K - $197K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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