Interested in this AI/ML Engineer role at ConnectiveRx?
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Overview:
Being on medication is tough enough. We want to make getting it the easy part. Getting prescriptions to patients has become increasingly complex. When things get messy along the prescription journey, pharmaceutical manufacturers rely on us to untangle the process and create a clear path—allowing patients to build trusting relationships with their medication brands.
We’re not only committed to taking the pain out of the prescription process, but we’re also devoted to bringing the brightest minds together under one roof. We bring together diverse voices—engineers, pharmacists, customer service veterans, developers, program strategists and more—all with one vision. Each perspective and experience makes ConnectiveRx better than the sum of its parts.
The Director of AI Product Management will play a pivotal role within our product organization, leading AI\-enabled product development and overseeing the management and execution of client onboarding and ongoing modifications in the Affordability product line. This leader will be responsible for scaling Copay and Buy \& Bill solutions through automation, analytics, and applied AI, driving measurable efficiency gains and high\-quality client outcomes. The Director will foster strong internal and external partnerships and will directly supervise a team of business analysts, providing guidance, mentorship, and strategic direction to support business objectives.
Responsibilities:
- Lead the planning, coordination, and execution of client implementations and change requests, incorporating AI\-assisted workflows and automation to improve speed, quality, and consistency.
- Serve as the primary point of contact for clients during onboarding and throughout the lifecycle of client engagements, translating client needs into an AI\-enabled product roadmap and measurable outcomes.
- Manage and develop a team of business analysts, upskilling the team on AI\-assisted analysis, prompt best practices, and data\-driven delivery methods while ensuring high performance and professional growth.
- Partner with cross\-functional teams including Product, Operations, Engineering/IT, Data Science, and Client Implementation to design, build, and operationalize AI capabilities that align with product strategy and client requirements.
- Define and track success metrics such as cycle\-time reduction, first\-time\-right rates, automation coverage, and client satisfaction, ensuring timelines, budgets, and deliverables are met.
- Identify, prioritize, and deliver AI efficiency initiatives including workflow automation, decision support, intelligent quality assurance, and self\-service capabilities through experimentation, iteration, and continuous improvement.
- Maintain up\-to\-date knowledge of AI/ML trends, responsible AI practices, and industry and regulatory changes impacting affordability programs, data usage, and automation.
Qualifications:
Education
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- Bachelor’s degree in Business, Computer Science, Data Analytics, Healthcare Administration, or a related field required.
- Master’s degree preferred.
Experience
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- Minimum of 7 years of experience in product delivery, client implementations, project or program management, or business analysis.
- Experience delivering AI\-enabled products or automation initiatives strongly preferred.
- Experience managing and developing teams preferred.
Knowledge, Skills, and Abilities
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- Knowledge of healthcare regulations and industry standards, with working familiarity in data governance, privacy, and responsible AI considerations, including model risk, bias, and explainability within regulated environments.
- Understanding of modern analytics and AI product practices, including feature instrumentation, A/B testing, human\-in\-the\-loop workflows, and collaboration with engineering and data science teams.
- Strong leadership skills with experience managing and mentoring teams and driving AI adoption and change management across business and technical stakeholders.
- Excellent communication, organizational, and problem\-solving skills with the ability to translate complex AI concepts into clear business value and operational requirements.
- Demonstrated ability to manage multiple initiatives simultaneously in a fast\-paced environment while using metrics and experimentation to prioritize work and measure impact.
Competencies
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- Authentic Leadership – Gains the confidence and trust of others through honesty, integrity, authenticity, self\-awareness, and situational adaptability. Honors commitments, maintains confidentiality, and models high standards of ethical behavior.
- Drives Results and Manages Execution – Takes initiative, maintains a strong focus on outcomes, and persists through challenges to achieve goals. Demonstrates urgency, accountability, and a commitment to meeting deadlines.
- Influence – Navigates complex organizational dynamics and effectively communicates vision and purpose. Builds alignment across stakeholders and drives organizational buy\-in.
- Analytical and Logical Reasoning – Uses critical thinking and data analysis to identify root causes, evaluate information, and develop effective solutions.
- Decision Quality – Makes informed, timely decisions while ensuring compliance with company policies, practices, and core values.
Travel Requirements
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- Willingness to travel to ConnectiveRx locations and client sites as needed.
Compliance Requirements
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- Adhere to all company policies, procedures, and training requirements consistent with ConnectiveRx Information Security and Compliance Programs, including SOC1, SOC2, PCI, and HIPAA requirements.
- Maintain strict compliance with company and client policies regarding business rules, ethics, and all applicable local, state, and federal laws and regulations.
Compensation \& Benefits: This position offers opportunities for a bonus (or commissions), with total compensation varying based on factors such as location, relevant skills, experience, and capabilities.
Employees at ConnectiveRx can access comprehensive benefits, including medical, dental, vision, life, and disability insurance. The company regularly reviews and updates its health, welfare, and fringe benefit policies to ensure competitive offerings. Employees may also participate in the company’s 401(k) plan, with employer contributions where applicable.
Time\-Off \& Holidays: ConnectiveRx provides a flexible paid time off (PTO) policy for exempt employees, covering sick days, personal days, and vacations. PTO is determined based on an employee’s first year of service. Employees also receive eight standard company holidays and three floating holidays annually, with prorations applied in the first year.
The company remains committed to providing competitive benefits and reserves the right to modify employee offerings, including PTO, STO, and holiday policies, in accordance with applicable laws and regulations.
Posted Salary Range: USD $169,600\.00 \- USD $218,800\.00 /Yr.
Salary Context
This $169K-$218K 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 ConnectiveRx, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($194K) sits 11% below the category median. Disclosed range: $169K to $218K.
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.
ConnectiveRx AI Hiring
ConnectiveRx has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Whippany, NJ, US. Compensation range: $145K - $218K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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