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About This Role
We are a healthcare technology company that provides platforms and solutions to improve the management and access of cost\-effective pharmacy benefits. Our technology helps enterprise and partnership clients simplify their businesses and helps consumers save on prescriptions.
As a leader in SaaS technology for healthcare, we offer innovative solutions with integrated intelligence on a single enterprise platform that connects the pharmacy ecosystem. With our expertise and modern, modular platform, our partners use real\-time data to transform their business performance and optimize their innovative models in the marketplace.
Position Summary:
The Principal AIOps Engineer will build the platform that makes AI cheap, fast, safe, and observable at RxSense. As a direct report to the Director of AI Engineering, this role will own the infrastructure that every AI\-powered product at RxSense depends on. This is a hands\-on\-keyboard position from day one, partnering with AI engineers, software engineers, data scientists, security, and finance to deliver deployment pipelines, agent runtime, eval frameworks, self\-hosted model serving, and the developer harness that determines how fast every other engineer in the company can ship.
Essential Duties and Responsibilities:
- Build and maintain end\-to\-end deployment pipelines for AI\-powered applications, including artifact builds, environment promotion, rollback, and observability hooks. Drive new greenfield deployment platforms from initial build to the default that AI teams ship on.
- Stand up and operate the runtime and lifecycle infrastructure for production agents, including deployment, versioning, monitoring, rate\-limiting, and retirement. Define the deployment contract (config, secrets, tools, memory, evals) and the operational SLOs.
- Own how the organization provisions, rotates, scopes, and meters access to model provider APIs (Anthropic, OpenAI, and others). Build a key management layer that enforces per\-team and per\-app quotas, prevents leakage, and gives finance and engineering a clear view of spend.
- Build evals into the CI/CD pipeline so no agent or LLM\-powered service ships without passing a defined eval bar. Design the framework so product teams can author their own evals against a shared harness, and so eval results gate promotion across environments.
- Stand up self\-hosted inference for workloads where managed APIs aren't the right fit, including latency\-sensitive paths, regulated data, cost optimization, and vendor redundancy. Own the serving stack, the autoscaling and GPU economics behind it, and the playbook for when a workload belongs to a managed provider versus internal infrastructure.
- Design and build the shared developer harness that every AI\-powered service uses: prompt management, model routing, retries, tracing, eval hooks, and policy enforcement. Set the abstractions that determine how fast every other AI engineer can ship for the next three years.
- Partner with finance on cost visibility, including token accounting, per\-feature cost attribution, and real\-time spend observability.
- Write documentation, runbooks, and clear interfaces so the platform is adoptable by other engineering teams without hand\-holding.
- Participate in code review and promote collaboration and best practices including simplicity, automation, sound design patterns, test coverage, and reusability.
Education/Experience/Competencies:
- BS (or higher, e.g., MS or Ph.D.) in Computer Science or related technical field involving coding, or equivalent technical experience.
- 6\+ years of platform, infrastructure, or DevOps engineering, with at least 2 years building production infrastructure for AI/ML or LLM\-powered systems. We care more about depth and drive than years on a resume.
- Deep hands\-on experience designing and operating CI/CD pipelines for high\-velocity engineering organizations, including artifact management, environment promotion, and progressive rollout.
- Strong AWS background, comfortable down to the IAM, networking, and container orchestration layers.
- Proven track record building developer platforms or internal tools that other engineering teams adopted by choice, not by mandate.
- Production experience with LLM\-powered applications, including prompt management, model routing, retries, tracing, and the operational realities of running agents or chains in production.
- Hands\-on coding fluency in Python or TypeScript, ideally both. This is a keyboard role, not an architecture\-only role.
- Comfortable operating in a polyglot environment. The RxSense AI engineering stack spans Python, .NET, and TypeScript, and you will deploy and support services across all three.
- Comfortable owning the cost and reliability conversation with both engineering leadership and finance partners.
- Strong written communication and a bias toward documentation, runbooks, and clear interfaces.
- Proven analytical thinking and problem\-solving skills.
- Excellent communication skills, both verbal and written.
Bonus Qualifications:
- Direct experience integrating with Anthropic, OpenAI, or other frontier model provider APIs at scale, including key management, quota enforcement, and capacity planning.
- Hands\-on experience self\-hosting models with vLLM, TGI, SGLang, or similar inference servers, including GPU autoscaling and cost optimization.
- Built or contributed to an eval framework that gated production deployments.
- Familiarity with agent runtimes and frameworks such as the Claude Agent SDK, LangGraph, or in\-house equivalents.
- Working familiarity with .NET, enough to read code, debug a deploy, and pair with service owners.
- Background in healthcare, PBM, pharmacy, or another regulated data environment.
- FinOps experience, particularly attributing AI spend to features or business units.
- Kubernetes operator experience or comfort with custom controllers.
- Experience with Agile development methodologies, preferably both Scrum and Kanban
Salary Range: $190,000 \- $225,000
RxSense believes that a diverse workforce is a more talented and productive workforce. As such, we are an Equal Opportunity and Affirmative Action employer. Our recruitment process is free from discriminatory hiring practices and all qualified applicants are considered for employment without regard to race, color, religion, sex, gender, sexual orientation, gender identity, ancestry, age, or national origin. Neither will qualified applicants be discriminated against on the basis of disability or protected veteran status. We believe in the strength of the collaboration, creativity and sense of community a diverse workforce brings.
Salary Context
This $190K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At RxSense, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($207K) sits 5% below the category median. Disclosed range: $190K to $225K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
RxSense AI Hiring
RxSense has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $225K - $225K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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