Interested in this AI/ML Engineer role at HP?
Apply Now →Skills & Technologies
About This Role
Distinguished Technologist - Enterprise Integration and Platform Native AI Description -
Role Summary
We are seeking an exceptional Distinguished Technologist – Enterprise Integration & AI to lead HP’s enterprise integration and AI architecture at global scale. This role serves as a technical authority, strategic thought leader, and innovation driver , responsible for shaping HP’s integration fabric, API economy, and AI enablement strategy across Digital Tech.
The Distinguished Engineer will define long-term architecture vision, influence enterprise-wide technology decisions, and ensure scalable, secure, and AI-ready platforms that accelerate business outcomes. This role operates at the intersection of integration, APIs, data, and AI , driving foundational capabilities that power HP’s digital transformation and agentic AI future.
Key Responsibilities
API & AI-First API Strategy and Governance
- Define and lead HP’s AI-first API strategy , ensuring all enterprise APIs are discoverable, reusable, and optimized for machine and agent consumption
- Institutionalize the API-as-a-Product operating model, with clear ownership, lifecycle governance, and measurable business value
- Own and enforce enterprise-wide API governance standards, covering design, lifecycle, security, and AI readiness
- Define and operationalize the AI-Ready API Framework , including automated governance gates to ensure production readiness
- Enable HP’s Unified API & Agentic AI Strategy , positioning APIs as the foundation for enterprise AI adoption
- Establish standards for emerging integration paradigms, including event-driven architectures and MCP (Model Context Protocol)
Platform AI Enablement & Governance
- Orchestrate AI enablement and adoption across strategic platforms (SAP, ServiceNow, Adobe, MuleSoft, NOVA)
- Own the enterprise AI/ML governance process, including AI board reviews, privacy, risk, and cybersecurity compliance
- Evaluate and guide platform-native AI capabilities to ensure alignment with enterprise architecture and compliance requirements
- Lead cross-functional AI enablement initiatives to drive consistent adoption and measurable business outcomes
- Align platform AI initiatives with enterprise prioritization frameworks (e.g., Digital AI Nerve Center)
- Evaluate AI tooling, vendor solutions, and partner ecosystems for alignment with integration and platform strategy
- Enable AI-driven SDLC adoption for partner ecosystems (e.g., NOVA vendors), including onboarding, compliance, and knowledge integration
Lead/ support key tech initiatives
- Lead evaluation of new and emerging tech, conduct POC, and lay down path for adoption.
- Lead the EPS AI initiatives through AI/ML review board and cyber approval process.
- Partner with Applied AI teams for knowledge sharing of Enterprise AI initiatives
- Coach and mentor teams in their AI journey
- Improve reuse, reduce duplication, and strengthen governance via API-as-a-product discipline
- Enable scalable, enterprise-grade AI products (e.g., IBDF, AIOps) delivering measurable business value
Qualifications & Experience
- 15+ years of experience in enterprise architecture, integration, and platform engineering
- Proven track record defining and implementing enterprise integration strategies and API ecosystems at scale
- Deep expertise in integration patterns (event-driven, messaging, APIs, microservices) and cloud-native architectures
- Experience leading AI/ML enablement, governance, and platform integration in large enterprises
- Strong understanding of enterprise platforms (SAP, ServiceNow, Adobe, MuleSoft) and their AI capabilities
- Demonstrated ability to influence executive stakeholders and drive cross-functional alignment
- Recognized as a thought leader and technical authority , with experience mentoring senior architects and engineers
Leadership Expectations
- Act as a trusted advisor to executive leadership on enterprise architecture and AI strategy
- Drive innovation by identifying emerging technologies and translating them into enterprise capabilities
- Mentor and elevate the technical community, fostering architectural excellence across HP
Represent HP externally with partners, customers, and industry forums as a leader in integration and AI *
Education & Experience Recommended
- Four-year or Graduate Degree in Computer Science, Information Technology, Software Engineering, or any other related discipline or commensurate work experience or demonstrated competence.
- Typically has 12+ years of work experience, preferably in software designing & development, software architecture, programming languages, or a related field.
Preferred Certifications
- Programming Language/s Certification (Java, C++, Python, JavaScript, or similar)
Knowledge & Skills
- Agile Methodology
- Amazon Web Services
- Application Programming Interface (API)
- Automation
- C++ (Programming Language)
- Computer Science
- DevOps
- Docker (Software)
- Full Stack Development
- Java (Programming Language)
- JavaScript (Programming Language)
- Kubernetes
- Microservices
- Microsoft Azure
- Python (Programming Language)
- React.js
- Scalability
- Software Development
- Software Engineering
- SQL (Programming Language)
Cross-Org Skills
- Effective Communication
- Results Orientation
- Learning Agility
- Digital Fluency
- Customer Centricity
Impact & Scope
- Impacts multiple functions and influences long-term strategic direction across multiple disciplines or areas of HP.
Complexity
- Invents, develops and introduces new methods and techniques that impact multiple disciplines and work groups.
Disclaimer
- This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.
Job -
Software Schedule -
Full time Shift -
No shift premium (United States of America) Travel -
Relocation -
Equal Opportunity Employer (EEO) -
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "
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 HP, 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.
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.
HP AI Hiring
HP has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Spring, TX, US, Vancouver, WA, US, Spring, TX. Compensation range: $72K - $159K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.