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About This Role
Sumitomo Pharma Co., Ltd., is a global pharmaceutical company based in Japan with operations in the U.S. (Sumitomo Pharma America, Inc.), focused on addressing patient needs in oncology, urology, women's health, rare diseases, cell \& gene therapies and CNS. With several marketed products and a diverse pipeline of early\- to late\-stage investigational assets, we aim to accelerate discovery, research, and development to bring novel therapies to patients sooner. For more information on SMPA, visit our website https://www.us.sumitomo\-pharma.com
The Senior AI Platform Architect is a senior individual contributor responsible for partnering with business and technology stakeholders to identify, define, and prioritize AI opportunities, then architect and deliver working implementations in a secure, reliable, and compliant manner. This role designs and evolves the enterprise AI platform and delivery patterns spanning traditional ML and modern LLM\-based solutions, enabling teams to move from use case to production through reusable components, MLOps/LLMOps practices, and automation. The ideal candidate has strong hands\-on engineering skills (Python, Terraform), deep knowledge of AI/ML/LLMs, and a track record of translating business problems into measurable use cases and production\-ready systems.
Job Duties and Responsibilities
- Lead end\-to\-end AI use case delivery: facilitate discovery with business partners, define problem statements, success metrics, data needs, constraints, and deliver working prototypes that mature into production implementations.
- Architect and evolve an enterprise AI/ML/LLM platform including reference architectures, reusable patterns, and paved roads for model training, evaluation, deployment, and monitoring (MLOps/LLMOps).
- Design and implement LLM solutions such as RAG, tool/function calling, prompt management, agent workflows, and safety guardrails; select appropriate models and approaches based on use case requirements.
- Define data and integration patterns for AI solutions, including feature access, document ingestion pipelines, vector search, APIs/eventing, and secure connectivity to internal systems.
- Build automation and infrastructure\-as\-code to provision and operate AI workloads using Terraform (e.g., cloud resources, IAM, networking, storage, observability) and Python (services, pipelines, tooling).
- Establish and enforce engineering standards and best practices for AI systems: reproducibility, testing, model evaluation, documentation, cost controls, and operational readiness.
- Partner with security, privacy, and compliance teams to implement governance controls for AI (e.g., access management, data handling, auditability, model risk management, validation requirements).
- Own reliability and performance for AI platform services by designing for resilience and scalability, instrumenting telemetry, and driving incident response and root cause remediation.
- Enable and guide application and data teams through design reviews, hands\-on pairing, internal documentation, and reference implementations.
- Communicate architecture decisions and tradeoffs clearly to technical and non\-technical stakeholders; manage competing priorities to deliver outcomes aligned with business value.
Key Core Competencies
- Build relationships and rapport with key internal and customer contacts.
- Customer service mindset.
- Attentive problem solver.
- Ability to clearly present complete problem details and propose recommended solutions to management.
- Understanding of the importance of and compliance to Security and Compliance in IT environments.
Education and Experience
- BS in Information Technology or related field.
- 8\+ years of experience designing and delivering cloud platforms and/or production software, including 3\+ years building or operating AI/ML solutions (traditional ML and/or LLM\-based systems) in a regulated environment.
- Experience working with multi\-tiered ticket handling/resolution systems.
- Strongly motivated, well\-organized, and shows professional initiative.
- Takes ownership of assignments, can work both independently and as part of the team.
- Ability to work in a fast\-paced and challenging environment.
- Excellent organizational skills with the ability to manage multiple tasks simultaneously.
- Exceptional written and oral communication skills.
- Ability to work responsibly with minimal supervision and with a sense of urgency while maintaining the proper judgment on when to inform management on an issue and escalate concerns or customer escalations.
- Display ability to solve problems.
- Deep knowledge of AI/ML fundamentals and LLM architectures and patterns, with strong hands\-on experience in Python and Terraform; familiarity with CI/CD and major cloud platforms (AWS and/or Azure) for production AI workloads.
The base salary range for this role is
$162,500\.00 \- $203,100\.00
Base salary is part of our total rewards package which also includes the opportunity for merit\-based salary increases, short incentive plan participation, eligibility for our 401(k) plan, medical, dental, vision, life and disability insurances and leaves provided in line with your work state. Our robust time\-off policy includes flexible paid time off, 11 paid holidays plus additional time off for a shut\-down period during the last week of December, 80 hours of paid sick time upon hire and each year thereafter. Total compensation, including base salary to be offered, will depend on elements unique to each candidate, including candidate experience, skills, education and other factors permitted by law.
Disclaimer: The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. All personnel may be required to perform duties outside of their normal responsibilities from time to time, as needed.
Confidential Data: All information (written, verbal, electronic, etc.) that an employee encounters is considered confidential, in accordance with applicable law.
Compliance: Achieve and maintain Compliance with all applicable regulatory, legal and operational rules and procedures, by ensuring that all plans and activities for and on behalf of Sumitomo Pharma America (SMPA) and affiliates are carried out with the "best" industry practices and the highest ethical standards.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Mental/Physical Requirements:
Fast\-paced environment handling multiple demands is involved. Must be able to exercise appropriate judgment as necessary. Requires a high level of initiative and independence. Excellent written and oral communication skills required. Requires ability to use a personal computer for extended periods of time.
Travel Requirements:
Primarily remote role with periodic on\-site meetings in office. Must be able to travel domestically and internationally as needed.
Drug Screening Requirements
Applicants for sales/field, manufacturing, or other designated roles will be required to submit to a pre\-employment drug test.
Sumitomo Pharma America (SMPA) is an
Qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.
Sumitomo Pharma America (SMPA) endeavors to make its application process accessible to all. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact SMPA at [email protected]\-pharma.com. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
### Our Vision and Culture
At Sumitomo Pharma America, our work is guided by the Sumitomo Pharma mission, vision and values, which tie closely to our company’s cultural pillars.
Our Mission
*To broadly contribute to society through value creation based on innovative research and development activities for the betterment of healthcare and fuller lives of people* *worldwide*
Our Vision
*For Longer and Healthier Lives, we unlock the future with cutting edge technology and* *ideas*
Salary Context
This $162K-$203K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sumitomo Group, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($182K) sits 16% below the category median. Disclosed range: $162K to $203K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sumitomo Group AI Hiring
Sumitomo Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $203K - $203K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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