Interested in this AI/ML Engineer role at Panasonic?
Apply Now →About This Role
Overview:
Every moment of every day, people all over the world turn to Panasonic to make their lives simpler, more enjoyable, more productive and more secure. Since our founding almost a century ago, we’ve been committed to improving peoples’ lives and making the world a better place–one customer, one business, one innovative leap at a time. Come join our journey!
Responsibilities:
Lead Business Analyst, AI \& Automation
https://www.youtube.com/watch?v\=0tMgKm\_71qs
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Meet the Recruiter:Amber SmallwoodWhat You'll Get To Do:
Provides operational leadership for enterprise AI, agentic AI, and automation initiatives. Responsible for leading complex business analysis efforts, shaping AI and automation roadmaps, mentoring business analysts, and partnering with business and technology leaders to deliver measurable business outcomes. Acts as a senior subject matter expert and thought leader across Intelligent Automation, Agentic AI solutions, and Business Process Transformation.
Strategic Business \& AI Partnership
- Build and sustain trusted advisory relationships with business unit executives and management.
- Serve as Lead Subject Matter Expert for enterprise AI, Agentic AI, and Process Automation initiatives across lines of business, including enterprise enablement of Microsoft Copilot and Copilot‑extended solutions.
- Translate business strategy into AI\- and automation\-enabled operating models.
- Represent and communicate enterprise AI \& Automation strategy, value realization, and roadmap to business stakeholders.
- Act as the primary escalation point for complex, cross\-domain AI \& Automation initiatives.
Enterprise AI, Agentic AI, \& Automation Delivery Leadership
- Lead identification, evaluation, and prioritization of automation and Agentic AI opportunities.
- Guide solution design leveraging Robotics Process Automation (RPA), Desktop Automation, Intelligent Document Processing (IDP), Conversational AI, Machine Learning (ML), and Agentic AI patterns (autonomous agents, orchestrated workflows, tool\-using agents).
- Ensure business requirements, process flows, and solution designs enable responsible, secure, and scalable AI deployment.
- Oversee development of Standard Operating Procedures (SOPs) and Process Definition Documents (PDDs).
- Partner with architecture, security, data, and platform teams to ensure compliance with enterprise standards.
Operational Leadership \& Governance
- Lead demand intake, intake triage, and capacity planning for AI \& Automation services.
- Enforce governance models for AI and automation lifecycle management.
- Track benefits realization, operational KPIs, and value delivery for AI\-enabled and automated processes.
- Provide consolidated executive reporting, issue escalation, and corrective action planning.
- Contribute to AI risk management, model governance, ethical AI practices, and control design.
Team Leadership \& Capability Building
- Provide formal and informal leadership and mentorship to Business Analysts and 3rd party developers.
- Establish analysis standards, reusable artifacts, and best practices for AI and automation initiatives.
- Review and guide business cases, requirements, and solution proposals produced by team members.
- Support capability building through training, communities of practice, and tool adoption.
- Influence workforce strategy through skill development aligned to AI and Agentic AI adoption.
Scope:
- Lead complex initiatives spanning multiple business units.
- Provide functional leadership to analysts and developers.
- Influence investment decisions and enterprise AI/Automation roadmap.
- Manage business relationships for internal Business Unit customers, understand customer processes and needs in order define and deliver appropriate AI \& Automation services and coordinate resultant project activity.
- Understand and prepare business requirements, project plans and resource needs, and coordinate work assignments
- Ensure compliance with all policies and procedures
Qualifications:
What You'll Bring:
Education \& Experience:
- Bachelor’s degree in Business, Information Systems, Engineering, Data Analytics, or related field required.
- 8\+ years of IT Business Analysis, Process Automation, or Digital Transformation experience required.
- Demonstrated experience delivering advanced Automation, AI, and Agentic AI solutions in enterprise environments.
- Proven success providing operational leadership, mentoring, and leading cross\-functional teams.
- Experience defining and governing AI\-enabled business processes preferred.
- Certifications in one or more of the following preferred: ITIL, PMP, CBAP, BRM, Six Sigma, AI/ML\-related certifications.
Technical \& Domain Experience (Preferred)
- Agentic AI frameworks and patterns (goal\-based agents, tool\-use agents, human\-in\-the\-loop designs).
- RPA platforms (e.g., UiPath) and workflow orchestration tools.
- Process modeling and discovery tools (e.g., IBM Blueworks).
- ServiceNow or comparable enterprise service management platforms.
- Experience enabling business solutions using Microsoft Copilot (Microsoft 365, Copilot Studio) and Copilot extensibility in partnership with platform and architecture teams.
- Data, analytics, and AI governance concepts.
Competencies:
- Strategic thinking and systems\-level problem solving.
- Ability to translate ambiguous business needs into actionable AI\-enabled solutions.
- Strong executive communication and influence skills.
- Comfort operating with ambiguity and emerging technologies.
- Commitment to responsible AI, operational rigor, and value realization.
- Collaborative leadership across business, data, security, and technology domains.
- Able to collaborate and work together with internal and external partners effectively to complete team assignments and represent BU needs and demands.
- Able to represent Panasonic services that can address a defined Business Unit need and develop the business case that illustrates the Return on Investment (ROI).
- Communicate and work effectively with others.
Communications:
- Regular communications with all levels of customer/business unit and IT management.
- Regular communications with 3rd party service providers and vendors.
- Professionally represent the team as a trusted advisor and strategic partner.
- Model Panasonic’s Basic Business Principles in all interactions.
Other Requirements:
- Up to 5% travel as required
Benefits \& Perks \- What's In It For You:
Panasonic prioritizes total well\-being and offers comprehensive benefits options to support physical, emotional, financial, social, and environmental health:
- Health Benefits – Offering medical, dental, vision, prescription plans, plus Health Savings Account and Flexible Spending Account options.
- Voluntary Benefits – Life, accident, critical illness, disability, legal, identity theft, and pet insurance.
- Panasonic Retirement Savings \& Investment Plan (PRSIP) – 401(k) plan with company matching contributions and immediate vesting.
- Paid Time\-Off Benefits – Vacation, holidays, personal days, sick leave, volunteer, and parental \& caregiver leave.
- Educational Assistance – Tuition reimbursement for job\-related courses after six months of service.
- Health Management and Wellbeing Programs –Lifestyle Spending Account, EAP, virtual health management, chronic condition, neurodiversity, tobacco cessation, substance abuse support, and life stage and fertility resources. Available to eligible employees starting the first day of the month following your start date. Eligibility for each benefit may vary based on employment status, location, and length of service.
- Employee Recognition Program \- High5 employee recognition and awards platform, quarterly and annual employee recognition
- Annual Bonus Program \- Opportunity for an annual performance\-based bonus.
We Take Opportunity Seriously:
At Panasonic, we are committed to a workplace that genuinely fosters inclusion and belonging. Fairness and Honesty have been part of our core values for more than 100 years and we are proud of our diverse culture as an equal opportunity employer.
The wage range of $115,000 \- $125,000 is just one component of Panasonic’s total package. Actual compensation varies depending on the individual’s knowledge, skills, experience, and location. This role may be eligible for discretionary bonuses and incentives.
We understand that your career search may look different than others and embrace the professional, personal, educational, and volunteer opportunities through which people gain experience. If you are actively looking or starting to explore new opportunities, send us your application!
*Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or other characteristic protected by law. All qualified individuals are required to perform the essential functions of the job with or without reasonable accommodation.*
*Due to the high volume of responses, we will only be able to respond to candidates of interest. All candidates must have valid authorization to work in the U.S.*Thank you for your interest in Panasonic.
\#LI\-AS1
\#LI\-REMOTE
Salary Context
This $115K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Panasonic, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($120K) sits 34% below the category median. Disclosed range: $115K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Panasonic AI Hiring
Panasonic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $125K - $125K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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