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About This Role
Commercial AI Lead with Pharma Services Group (PSG) \- REMOTE
We are looking to hire a candidate with the mentioned skill sets and experience for one of our clients within the pharmaceutical Industry. This is a 6\+ month contracting role, with potential for extension. This is a REMOTE role.
Role Summary:
There is an immediate requirement from the client. The core need is for a resource with strong experience in commercial transformation, specifically within CDMO and strong AI experience.
The Commercial AI Lead will drive the strategy, design, and delivery of AI\-enabled commercial capabilities across PSG. This role will partner with Commercial, Sales Operations, Digital/IT, Data, Legal, Compliance, and platform teams to identify high\-value AI opportunities, translate them into scalable solutions, and accelerate adoption across commercial workflows.
The ideal candidate combines deep commercial systems knowledge, practical GenAI and agentic AI fluency, strong enterprise integration judgment, and the ability to lead cross\-functional teams from concept through measurable business impact.
Key Responsibilities:
- Define and lead the PSG Commercial and Finance AI roadmap across
priority workflows such as lead\-to\-order, quote\-to\-cash, opportunity management, pricing, quoting, forecasting, customer service, and commercial operations.
- Identify and prioritize high\-value AI use cases by partnering with business stakeholders to assess value, feasibility, complexity, risk, adoption potential, and measurable commercial impact.
- Design practical enterprise AI solutions using patterns such as Agentic RAG, multi\-agent workflows, tool\-using agents, human\-in\-the\-loop escalation, and AI\-assisted commercial decision support.
- Lead AI integration across commercial systems, including SAP, Salesforce/SFDC, CPQ, ERP, CRM, pricing, customer, product, contract, and order management platforms.
- Evaluate and apply enterprise AI platform capabilities, including Salesforce Agentforce, SAP Joule, Microsoft Copilot, ChatGPT Enterprise, ServiceNow AI, and comparable commercial GenAI offerings.
- Partner with IT and data teams on AI architecture, including APIs, MCP, agent tools/skills, middleware, identity, permissions, semantic search, knowledge bases, data quality, and governance.
- Establish responsible AI practices for commercial use cases, including privacy, security, access controls, validation, auditability, hallucination risk mitigation, and compliance with enterprise standards.
- Own product delivery and value realization, including MVP definition, requirements, backlog prioritization, pilot execution, adoption plans, KPI tracking, and scale\-up recommendations.
- Drive change management and adoption through stakeholder engagement, user enablement, feedback loops, playbooks, communications, and trust\-building with commercial teams.
- Communicate clearly with senior leaders on AI opportunities, risks, tradeoffs, investment needs, delivery progress, and realized business value.
Required Skills/Qualifications:
- Significant experience in commercial systems, commercial operations, digital transformation, enterprise applications, AI product management, or related business technology roles.
- Strong understanding of commercial processes and systems such as Salesforce/SFDC, SAP, CPQ, ERP, CRM, pricing, quoting, order management, and quote\-to\-cash.
- Practical knowledge of Generative AI, agentic AI patterns, RAG, workflow automation, AI assistants, and enterprise AI platforms.
- Experience translating business pain points into scalable digital or AI\-enabled solutions with measurable business outcomes.
- Experience in pharma services, life sciences, CDMO, biopharma, manufacturing, clinical supply, or regulated commercial environments.
- Experience with Salesforce Agentforce, SAP Joule, Microsoft Copilot, ChatGPT Enterprise, ServiceNow AI, or similar enterprise AI capabilities.
- Familiarity with MCP, agent tools, agent skills, APIs, semantic search, vector databases, knowledge management, and secure system integration.
- Experience leading AI pilots from discovery through launch, adoption, measurement, and scale.
- Familiarity with responsible AI practices, model evaluation, prompt/agent testing, data governance, and enterprise risk controls.
- Ability to work across business, technology, data, security, legal, compliance, and vendor teams in a large matrixed enterprise.
- Strong communication skills, including the ability to explain complex AI concepts, risks, and tradeoffs to non\-technical senior stakeholders.
- Bachelor’s degree in Business, Information Systems, Computer Science, Engineering, Data Science, or a related field; advanced degree preferred.
- Leadership Expectations: this role should model Thermo Fisher’s culture by bringing Integrity to responsible AI decisions, Intensity to execution and value delivery, Innovation to practical AI solution design, and Involvement through strong cross\-functional partnership.
Success Measures
- AI roadmap aligned to PSG commercial priorities and enterprise technology standards.
- High\-value AI use cases moved from concept to production with measurable outcomes.
- Improved commercial productivity, cycle time, quote speed, knowledge access, customer responsiveness, or decision quality.
- Strong adoption by commercial users and clear executive visibility into realized value.
- Responsible, secure, and scalable AI solutions embedded into core commercial workflows.
Other Job Details:
- *Job Type:* C2C or W2\.
- *Duration:* 6\+ months with high possibility of extension.
- *Location:* REMOTE.
- *Pay Rate:* $65/hr. on C2C. or $60/hr. on W2\.
- *Interviews:*Video interviews.
- *Docs required:*ID proof will be required.
Salary Context
This $124K-$135K range is in the lower quartile 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 OMG Technology, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($130K) sits 27% below the category median. Disclosed range: $124K to $135K.
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.
OMG Technology AI Hiring
OMG Technology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Union, NJ, US. Compensation range: $135K - $135K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 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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