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About This Role
Job Title: Principal Technical AI & Solutions Consultant
Job Function: Business Analyst
Location: Remote
About USDM
USDM Life Sciences is a premier consulting company with 20+ years of experience assisting heavily regulated biotech, medical device, and pharmaceutical companies with their GxP technologies to accelerate growth. Our deep domain knowledge and technology expertise in life sciences business processes are what set us apart. From strategy to implementation and adoption, we have delivered thousands of GxP projects globally.
As part of the USDM team, you have the opportunity to work with cutting-edge technologies through our many partnerships with companies like Microsoft, Google, Oracle, DocuSign, Box, and many more. From molecule to market, you will help connect technology, people, and data in new ways to generate real-time insights to improve business outcomes for USDM's customers. Are you ready to make an impact and drive real digital transformation in life sciences?
Founded in Santa Barbara in 1999, USDM has grown to a progressive, global company with 300+ remote employees and offices throughout the US, Canada, and Germany.
Nature and Scope of Job
The Principal Technical & AI Solutions Consultant is a senior practitioner and business driver who bridges deep technical implementation expertise with the strategic acumen required to generate and advance commercial opportunities within the life sciences industry. This role is uniquely hands-on. The individual must be equally comfortable architecting and implementing AI-powered solutions, configuring data pipelines, and executing system integrations as they are leading executive-level discovery sessions, crafting proposals, and driving sales cycles to close.
The Principal Technical & AI Solutions Consultant will serve as USDM's most technically credible commercial voice at the intersection of AI, data, and life sciences IT operations, translating customer challenges into tangible, deployable solutions that deliver measurable business and compliance value.
Primary Responsibilities
Business Development and Sales Leadership
- Drive business development by identifying, qualifying, and advancing new opportunities across the life sciences industry with a focus on AI implementation, data and integration architecture, and IT-enabled operations.
- Own the end-to-end sales cycle for technical and AI engagements, from initial customer discovery and solution scoping through proposal development, SOW authoring, RFP response, and contract execution.
- Cultivate and maintain executive-level customer relationships, conducting compelling C-suite and VP-level conversations that connect technical capabilities to strategic business and regulatory outcomes.
- Advise customers on the current AI landscape, including foundation model providers (OpenAI, Anthropic, Google, AWS), AI platforms, and life sciences-specific AI tooling, and translate this knowledge into practical, risk-managed deployment strategies.
- Develop and deliver technically rigorous yet business-accessible presentations, demonstrations, and proof-of-concept builds that accelerate customer decision-making.
AI, Data, and Integration Delivery
- Architect, implement, and deliver AI and machine learning solutions within life sciences environments, including large language model (LLM) deployment, retrieval-augmented generation (RAG) pipelines, AI agents, and machine learning model integration into validated systems.
- Design, build, and manage data pipelines that aggregate, transform, and deliver structured and unstructured data across cloud, on-premises, and hybrid environments (e.g., Azure Data Factory, AWS Glue, Databricks, Snowflake, dbt).
- Lead system integration efforts connecting enterprise platforms, including Veeva Vault, Salesforce, SAP, Oracle, MES, LIMS, and ERP systems, using RESTful APIs, message queues, and integration middleware.
- Configure and administer life sciences IT systems, supporting platform migrations, cloud infrastructure deployments, and validated system implementations in GxP-compliant environments.
Delivery Leadership and Team Development
- Lead cross-functional delivery teams through the full project lifecycle, ensuring technical quality, on-time delivery, and measurable outcomes.
- Define project scopes, technical architectures, and deliverables in alignment with customer expectations and USDM delivery standards.
- Build and mentor teams of technical consultants, establishing best practices in AI implementation, data engineering, and system integration within life sciences contexts.
Additional Responsibilities
- Contribute to USDM thought leadership by authoring technical content, presenting at industry forums, and developing reference architectures and point-of-view materials on AI and data topics.
- Stay current on the evolving AI landscape, including model releases, agentic frameworks, AI governance standards, and FDA guidance on AI/ML in regulated environments.
- Collaborate with quality, compliance, and validation practice teams to ensure AI and technical solutions meet GxP requirements and can be delivered with appropriate validation rigor.
- Support internal capability development by mentoring colleagues, contributing to technical accelerators and proven methodologies, and participating in USDM practice-building initiatives.
- Participate in strategic account planning, pipeline reviews, and capability presentations with practice leadership and sales teams.
Required Qualifications
- 10+ years of experience in technical consulting, solutions architecture, data engineering, or IT implementation, with a minimum of 3 years focused on AI or machine learning solution delivery.
- Proven track record of driving business development, managing technical sales cycles, and closing professional services or software engagements with life sciences customers.
- Demonstrated ability to lead executive-level technical conversations and translate complex architectural concepts into clear, outcome-oriented business narratives.
- Hands-on experience designing and deploying AI solutions, including LLM-based applications, RAG architectures, AI agents, and ML model operationalization (MLOps).
- Proficiency in building and managing data pipelines using modern data engineering tools (e.g., Azure Data Factory, AWS Glue, Databricks, dbt, Airflow, or equivalent).
- Strong experience with system integration patterns and technologies, including RESTful APIs, ETL/ELT frameworks, message-based architectures (Kafka, Service Bus), and enterprise integration platforms (MuleSoft, Azure Integration Services, or equivalent).
- Familiarity with life sciences platforms including Veeva Vault, Salesforce, SAP, Oracle, and/or LIMS/MES systems and their integration landscape.
- Working knowledge of GxP requirements, computer system validation (CSV/CSA), and 21 CFR Part 11 as they apply to AI and IT systems in regulated environments.
- Proficiency in at least one programming or scripting language (Python, SQL, JavaScript, or equivalent) used in data engineering or AI implementation contexts.
- Exceptional written and verbal communication skills, including demonstrated ability to craft proposals, SOWs, and RFP responses.
- Strong analytical, architectural, and problem-solving skills, with a track record of designing and delivering technically sound solutions to complex operational challenges.
Education & Certifications
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical field required.
- Master's degree or MBA preferred.
- Cloud platform certification (e.g., AWS Solutions Architect, Microsoft Azure Solutions Architect, Google Professional Data Engineer) preferred.
- AI or machine learning certification (e.g., AWS Certified Machine Learning, Google Professional ML Engineer, or equivalent) preferred.
- Project Management Professional (PMP) certification preferred.
Working Conditions
The working conditions described here are representative of those that must be met by an employee to successfully perform the essential responsibilities and functions of the job and are not meant to be all-inclusive. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential responsibilities and functions of the job.
Unless reasonable accommodations can be made, while performing this job the staff member shall:
- Operate primarily in a remote home-office environment with prolonged periods of sitting or standing at a desk and working on a computer.
- Operate standard office productivity equipment, including a computer, phone, scanner, and printer.
- Frequently communicate with customers and internal stakeholders via telephone, video conference, email, or instant message. Must be able to exchange accurate information in these situations.
- Travel to customer sites for onsite work as needed.
Equal Opportunity Statement
USDM Life Sciences is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Disclaimer
This job description is intended to describe the general nature and the level of the work being performed by the people assigned to this position. It is not intended to include every job duty and responsibility specific to the position. USDM Life Sciences reserves the right to amend and change responsibilities to meet business and organizational needs as necessary.
Compensation
Base Salary Range: USD $144,200 to $216,200
The salary range represents the anticipated low and high end of USDM's compensation range for this position. Actual base salaries will vary and will be based on various factors, such as the candidate's qualifications, skills, competencies, and proficiency for the role. The compensation described above is subject to change and could be higher or lower than the range described based on market survey data or budget.
Full-time employees are eligible for health, vision, and dental insurance, life insurance, short and long-term disability, hospital indemnity, accident, and critical care coverage.
Both full and part-time employees, who are at least 21 years of age, are eligible to participate in USDM's 401k plan. Full and part-time employees may be eligible for paid time off.
All employees are eligible for USDM's rewards and recognition program.
Salary Context
This $144K-$216K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At USDM Life Sciences, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $144K to $216K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
USDM Life Sciences AI Hiring
USDM Life Sciences has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $216K - $216K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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