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About This Role
We’re hiring at Pitney Bowes, where top talent builds meaningful careers and lasting impact. We Move fast, Deliver excellence, and Win together…that’s The Pitney Bowes way. Here, how we work matters just as much as what we achieve.
We’re looking for people who:
- Act with urgency, accountability, and purpose
- Deliver high quality work with consistency and pride
- Collaborate effectively and elevate those around them
- Focus on outcomes that drive impact and growth
Job Description:
You Are
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A senior technical leader who sets the direction for the organization’s AI and data architecture. You build the scalable, secure, and governed foundation that supports analytics, machine learning, and generative AI. You serve as the architecture authority for AI and data platforms and ensure alignment across business priorities, technology strategy, and delivery teams.
You Will
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- Lead enterprise AI and data strategy and own the architecture roadmap.
- Align AI and data initiatives with business goals and measurable value.
- Establish standards for scalable and reusable AI and data capabilities.
- Serve as a trusted advisor to technology and business leaders on AI strategy.
- Design modern data architecture including lakehouse, mesh, and hybrid models.
- Define enterprise data models, canonical schemas, metadata strategy, lineage, and integration patterns.
- Lead the development of a centralized and scalable enterprise data platform.
- Build AI and ML platform capabilities including MLOps and LLMOps.
- Enable consistent model lifecycle management from data ingestion through deployment and monitoring.
- Standardize tooling, frameworks, and infrastructure for AI delivery.
- Drive adoption of production‑grade AI patterns and reduce experimental silos.
- Define and enforce data governance including ownership, stewardship, quality, MDM, and lifecycle management.
- Resolve fragmentation and establish a single trusted data foundation.
- Embed responsible AI practices including transparency, fairness, and explainability.
- Partner with security and risk teams to protect sensitive data and models and mitigate AI‑related risks.
- Establish auditability and controls for AI systems.
- Lead architecture governance through reference architectures, patterns, and reusable components.
- Conduct architecture reviews for major data platforms and AI‑enabled applications.
- Partner with engineering, product, security, and operations teams to support a federated adoption model.
- Build and mentor a high‑performing team of architects and engineers.
- Drive collaboration through councils, governance forums, and working groups.
You Bring
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- Enterprise experience with 15 or more years in enterprise architecture, data architecture, or AI and ML platforms.
- Proven success building enterprise‑scale data and AI platforms.
- Experience driving AI adoption from concept to production at scale.
- Strong background in AWS, Azure, GCP, and distributed systems.
- Technical depth across lakehouse, data mesh, ETL and ELT, streaming pipelines, model lifecycle management, MLOps, generative AI, LLM integration, metadata, lineage, and cloud‑native architectures.
- Understanding of security and compliance requirements for data and AI systems.
- Ability to operate at both strategic and hands‑on technical levels.
- Experience establishing enterprise standards and governance.
- Proven ability to influence senior stakeholders and cross‑functional teams.
- Track record of building high‑talent technical teams.
Success Outcomes in the First 12 to 24 Months
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- Enterprise AI and data platform adopted across business units.
- Clear ownership and governance in place with reduced data fragmentation.
- Standardized AI delivery lifecycle with measurable improvements in speed and quality.
- Increased business impact from AI including revenue growth, cost efficiency, and improved decision quality.
- Strong architecture governance model that drives consistency and reuse.
Key Performance Indicators
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Business Impact
- AI‑driven revenue contribution and cost optimization.
- Adoption of AI and data capabilities across business units.
Platform and Delivery
- Time required to deploy AI models.
- Percentage of workloads using the standardized platform.
Data Quality and Governance
- Percentage of critical data assets with defined ownership.
- Improvements in data quality scores.
AI Effectiveness
- Model accuracy, drift reduction, and business outcome metrics.
- Return on investment for AI projects.
Risk and Compliance
- Percentage of AI systems under governance.
- Reduction in data and AI‑related risk incidents.
Location:
This is a hybrid role, with 4 days in the Shelton, CT office required. (No relocation assistance offered.)
Sponsorship:
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Must be legally authorized to work in the US. Employer will not sponsor position for employment visa status now or in the future (ex. H\-1B).
We will:
- Provide the opportunity to grow and develop your career
- Offer an inclusive environment that encourages diverse perspectives and ideas
- Deliver challenging and unique opportunities to contribute to the success of a transforming organization
- Offer comprehensive benefits globally (PB Benefits and Wellbeing Programs)
Pitney Bowes is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard for race, color, sex, religion, national origin, age, disability (mental or physical), veteran status, sexual orientation, gender identity, or any other consideration made unlawful by applicable federal, state, or local laws.
All qualified applicants, including Veterans and Individuals with Disabilities, are encouraged to apply.
All interested individuals must apply online. Individuals with disabilities who cannot apply via our online application should refer to the alternate application options via our Individuals with Disabilities link.
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 Pitney Bowes, 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.
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.
Pitney Bowes AI Hiring
Pitney Bowes has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Shelton, CT, US.
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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