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About This Role
Our team members are at the heart of everything we do. At Cencora, we are united in our responsibility to create healthier futures, and every person here is essential to us being able to deliver on that purpose. If you want to make a difference at the center of health, come join our innovative company and help us improve the lives of people and animals everywhere. Apply today!
Job Details
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Position Summary:
The Principal Architect, Salesforce Platform \& AI serves as the enterprise architecture leader responsible for the strategic direction, governance, and long\-term evolution of the Salesforce platform ecosystem and AI\-enabled capabilities.
This role establishes the architecture vision that enables Salesforce Sales Cloud, Service Cloud, Experience Cloud, Data Cloud, automation platforms, and emerging AI technologies to operate as a secure, scalable, and cohesive enterprise platform. The Principal Architect defines the technology strategy, architectural standards, and governance framework that drive consistency and reuse across business domains while enabling rapid innovation and delivery.
Working across business leadership, enterprise architecture, cybersecurity, data, integration, DevSecOps, and delivery organizations, this individual ensures that platform investments align with enterprise objectives, regulatory requirements, operational excellence, and responsible AI adoption.
This role provides technical leadership to globally distributed architecture and engineering teams and serves as the primary authority for enterprise\-level Salesforce architecture decisions.
Key Responsibilities:
*Enterprise Platform and AI Strategy*
- Define and continuously evolve the enterprise Salesforce platform strategy, target\-state architecture, and multi\-year technology roadmap.
- Establish a unified architecture vision spanning Sales, Service, Experience, Integration, Data, Automation, and AI capabilities.
- Maintain comprehensive visibility across Salesforce organizations, shared services, integrations, data domains, platform dependencies, technical debt, and operational risk.
- Define architectural approaches for enterprise AI adoption, including agent\-based capabilities, intelligent automation, data readiness, governance, and human oversight frameworks.
- Evaluate emerging Salesforce, AI, and cloud platform capabilities and determine strategic applicability and business value.
- Identify opportunities for platform consolidation, modernization, standardization, and cost optimization.
- Align platform investments with enterprise objectives including scalability, security, reliability, agility, and operational efficiency.
- Guide technology investment decisions and establish strategic architecture priorities across the Salesforce ecosystem.
*Architecture Governance and Technology Leadership*
- Lead architecture governance through the Design Authority, Architecture Review Board, Salesforce Center of Excellence, and enterprise governance forums.
- Define and maintain enterprise architecture standards, reference architectures, reusable patterns, technical guardrails, and decision frameworks.
- Govern architecture across application development, integrations, data architecture, identity management, security, DevSecOps, AI services, automation platforms, and operational tooling.
- Partner with Enterprise Architecture, Cybersecurity, Infrastructure, Data, and Integration teams to ensure alignment with enterprise standards.
- Drive architectural consistency across business domains while balancing innovation, delivery speed, and operational risk.
- Establish and manage architecture exception processes, decision records, and platform standards documentation.
- Resolve complex cross\-domain architectural challenges and influence strategic technology decisions across the enterprise.
*Platform Health and Operational Excellence*
- Own architecture accountability for platform scalability, performance, resilience, maintainability, security, and operational health.
- Establish measurable platform health indicators covering technical debt, release quality, security posture, adoption, reliability, and cost efficiency.
- Lead architecture strategy for environments, release management, DevSecOps practices, testing frameworks, observability, and operational readiness.
- Ensure alignment between architecture standards and production implementation through design reviews and governance checkpoints.
- Drive continuous improvement initiatives focused on platform simplification, automation, reliability, and engineering productivity.
- Sponsor development of reusable assets, accelerators, integration frameworks, and architecture playbooks.
*Leadership and Talent Development*
- Provide technical leadership and mentorship for architects and senior engineers across global and distributed teams.
- Influence executive stakeholders and technology leaders through clear communication of architecture strategy, risks, investment priorities, and business outcomes.
- Develop architecture capabilities, succession plans, and technical excellence programs across internal teams and strategic partners.
- Foster a culture of engineering discipline, innovation, accountability, and continuous learning.
- Act as the primary escalation point for complex architecture decisions impacting the enterprise Salesforce ecosystem.
Required Qualifications:
- 12\+ years of experience in enterprise architecture, platform architecture, solution architecture, or related technology leadership roles.
- 7\+ years of experience designing and governing Salesforce solutions within complex multi\-business\-unit or multi\-org environments.
- Deep expertise in Salesforce architecture, including security, identity, integration, data architecture, automation, platform scalability, and application lifecycle management.
- Demonstrated experience defining enterprise technology strategies, architecture roadmaps, governance models, and platform operating frameworks.
- Experience architecting AI\-enabled solutions, including governance, responsible AI, agent\-based architectures, data readiness, privacy, security, and human oversight.
- Proven success influencing executive stakeholders and leading cross\-functional decision\-making across large enterprise environments.
- Experience leading and mentoring globally distributed architecture and engineering teams.
- Strong communication skills with the ability to translate complex technical concepts into business\-focused recommendations and decisions.
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
*Preferred Qualifications:*
- Salesforce Certified Technical Architect (CTA) preferred.
- Salesforce Certified System Architect and Application Architect certifications.
- Experience with enterprise integration platforms.
- Strong background in DevSecOps, CI/CD, release governance, platform observability, and cloud security.
- Experience building or leading a Salesforce Center of Excellence (CoE).
- Experience in highly regulated industries such as healthcare, pharmaceutical distribution, life sciences, financial services, or manufacturing.
- Experience supporting global enterprise platforms with multi\-region delivery teams.
*Success Measures:*
- Execution of a clearly defined enterprise Salesforce and AI architecture strategy.
- Improved platform scalability, security posture, reliability, and operational performance.
- Increased reuse of enterprise capabilities and reduction of duplicate solutions across business domains.
- Reduction of technical debt and architecture exceptions through standardized patterns and governance.
- Successful and responsible adoption of AI capabilities aligned with enterprise risk and compliance standards.
- Increased delivery velocity through improved architecture frameworks, standards, and developer enablement.
- Growth and maturity of architecture and engineering capabilities across onshore and offshore teams.
What Cencora offers
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We provide compensation, benefits, and resources that enable a highly inclusive culture and support our team members’ ability to live with purpose every day. In addition to traditional offerings like medical, dental, and vision care, we also provide a comprehensive suite of benefits that focus on the physical, emotional, financial, and social aspects of wellness. This encompasses support for working families, which may include backup dependent care, adoption assistance, infertility coverage, family building support, behavioral health solutions, paid parental leave, and paid caregiver leave. To encourage your personal growth, we also offer a variety of training programs, professional development resources, and opportunities to participate in mentorship programs, employee resource groups, volunteer activities, and much more. For details, visit https://www.virtualfairhub.com/cencora
Full timeSalary Range\*
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$110,500 \- 220,990* This Salary Range reflects a National Average for this job. The actual range may vary based on your locale. Ranges in Colorado/California/Washington/New York/Hawaii/Vermont/Minnesota/Massachusetts/Illinois State\-specific locations may be up to 10% lower than the minimum salary range, and 12% higher than the maximum salary range.
Equal Employment Opportunity
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Cencora is committed to providing equal employment opportunity without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status or membership in any other class protected by federal, state or local law.
The company’s continued success depends on the full and effective utilization of qualified individuals. Therefore, harassment is prohibited and all matters related to recruiting, training, compensation, benefits, promotions and transfers comply with equal opportunity principles and are non\-discriminatory.
Cencora is committed to providing reasonable accommodations to individuals with disabilities during the employment process which are consistent with legal requirements. If you wish to request an accommodation while seeking employment, please call 888\.692\.2272 or email [email protected]. We will make accommodation determinations on a request\-by\-request basis. Messages and emails regarding anything other than accommodations requests will not be returned
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Affiliated Companies:
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Affiliated Companies: AmerisourceBergen Services Corporation
Salary Context
This $110K-$220K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cencora, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $110K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cencora AI Hiring
Cencora has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Conshohocken, PA, US, Philadelphia, PA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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