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About This Role
Cynosure is well positioned competitively to lead and transform the medical aesthetics industry. We are guided by our belief that this is a purpose-driven business, which improves patient’s lives and improves practitioner’s livelihoods.
- We are passionate about driving a healthy and growing medical aesthetics industry with leading-edge marketing which inspires consumers to engage in medical aesthetics.
- We are the leader in science-based technology which elevates the standard of care for consumers.
- We are relentlessly focused on being the undisputed innovation leader, addressing the growing demand for consumer treatments across all demographics.
- We are committed to building and strengthening our unmatched global footprint, with direct employees on five continents and in 15 countries and growing, and distributor partners serving over 130 countries.
- We put our people first and believe personal development drives business development.
- We love our customers and strive give them the leading-edge technology, service, marketing support and clinical education to improve their patient’s outcomes and to improve their livelihoods.
At Cynosure, we look for people who make an impact and encourage everyone to lead, create, and add value. In turn, we invest in our team to grow people’s careers and build upon our high-performance and culture-oriented organization. We work together as one team, winning together.
As we grow our global team, there is no better time to join us. As a market leader in medical aesthetics, Cynosure has over 25 years developing, manufacturing, and marketing products for dermatologists, plastic surgeons, medical spas, and healthcare practitioners. With the aesthetic industry’s most comprehensive product portfolio, our offerings span several categories including skin revitalization, body contouring, hair removal, and women’s health.
Our mission is to set Cynosure customers up for success shaping future leaders of the aesthetics industry and helping everyone who uses our technologies discover their version of beautiful.
Learn more about us by following our LinkedIn and Instagram sites and please visit us at www.Cynosure.com.
Job Summary
The Sr. AI Engineer (Forward Deployed) is a software engineer and technical consultant who works directly with business teams to solve high-value problems in their real-world operating environments. Embedded with functional stakeholders, this role translates ambiguous needs into working AI and automation solutions, guides opportunities from discovery and business-case development through prototyping, integration, governed production deployment, adoption, and value realization, and ensures that technology capabilities produce measurable business outcomes.
The initial focus will be activating and extending capabilities already available within Microsoft’s enterprise AI and automation ecosystem, including Microsoft 365 Copilot, Microsoft 365 Copilot Chat, Copilot agents, Copilot Studio, Power Automate, and related capabilities. The role will then help deliver purpose-built solutions integrated with enterprise applications and, as the organization’s AI maturity grows, support more advanced use cases requiring deeper AI engineering, systems integration, data, application development, and agentic solution skills. Success requires curiosity, strong consulting and facilitation skills, technical credibility, a bias for action, and a commitment to responsible AI.
Essential Duties and Responsibilities
The incumbent may be asked to perform other function-related activities in addition to the below-mentioned responsibilities as reasonably required by business needs.
- Partner with leaders, functional teams, and end users to understand business goals, map current workflows, identify pain points, and define opportunities for AI and automation.
- Facilitate discovery workshops and translate business needs into clear use cases, requirements, solution options, expected benefits, risks, and success measures.
- Assess and prioritize opportunities using a standardized business case that evaluates business value, cost, feasibility, data readiness, security, compliance, user impact, risk, time-to-value, scalability, and total cost of ownership.
- Design, prototype, code, configure, test, integrate, deploy, and support AI and automation solutions using Microsoft 365 Copilot, Microsoft 365 Copilot Chat, Copilot agents, Copilot Studio, Power Platform, Azure AI services, APIs, and appropriate software-development frameworks.
- Partner with the VP, AI Transformation, Information Systems (IS), business sponsors, and applicable governance stakeholders to provide technical feasibility, architecture, effort, dependency, and delivery-risk inputs while maintaining primary accountability for solution design and implementation. Support coordinated intake, prioritization, benefits tracking, and executive reporting as the organization’s AI operating model matures.
- Lead assigned solutions with a high degree of autonomy, balancing rapid experimentation with enterprise standards, documenting technical decisions, managing delivery commitments, and escalating material risks or dependencies early.
- Develop reusable prompts, agent instructions, workflows, connectors, templates, and solution patterns; maintain the associated documentation and inventory so approved capabilities can be discovered, governed, supported, and scaled across the organization.
- Collaborate with Information Systems (IS), Information Security, Legal, Privacy, Data, and business process owners to ensure solutions follow enterprise architecture, responsible AI, data protection, and access-control requirements, with clear business ownership, human oversight, production support, monitoring, and escalation responsibilities.
- Evaluate data, content, integration, and process readiness; design secure connections across systems such as Microsoft 365, Teams, SharePoint, CRM (Salesforce), ERP (SAP), service management (ServiceNow), and other enterprise platforms; identify dependencies; and recommend pragmatic remediation or phased delivery approaches.
- Build proofs of concept and minimum viable solutions, validate technical feasibility and business value, gather user feedback, iterate rapidly, and work with technical and business owners to harden successful pilots for secure, reliable, scalable, observable, maintainable, and supported production use, including documented architecture, testing, performance, continuity, and support requirements.
- Define and track adoption, quality, active usage, time and capacity recaptured, cycle-time reduction, risk, reuse, and business-value metrics; communicate outcomes, limitations, and lessons learned to business leaders and executive stakeholders.
- Enable users through demonstrations, office hours, documentation, training, community engagement, and practical guidance that builds AI fundamentals, use-case awareness, prompting skills, and safe adoption practices.
- Troubleshoot technical and operational issues, coordinate with internal teams and vendors, identify recurring user and platform needs, provide actionable feedback to technology owners, and continuously improve solution performance, reliability, usability, and maintainability.
- Stay current on emerging AI, agentic, automation, and Microsoft platform capabilities; inventory capabilities already licensed, evaluate vendor and architecture fit against consistent criteria, and recommend whether to extend an existing platform, buy a solution, build a custom capability, or engage an external partner.
- Embody and live our company Values.
- Ability to travel internationally as required (up to 15% of time).
Qualifications
Education
- Bachelor’s degree required
Specific Area of Study: Computer Science, Information Systems, Data Science, Engineering, or a closely related technical field.
Experience
- 5+ years of relevant experience
Specific Related Experience: Five or more years of experience in software engineering, solutions engineering, technical consulting, solutions architecture, or a related field, including hands-on delivery of AI, data, integration, low-code, or automation solutions. Demonstrated experience working directly with business stakeholders from discovery and business-case development through architecture, prototyping, production deployment, adoption, and value measurement is required. Hands-on experience with at least one enterprise AI platform is required; experience with Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, or comparable technologies is strongly preferred, along with experience evaluating whether to extend, buy, or build solutions.
Skills
- Exceptional consulting, active-listening, facilitation, and relationship-building skills; able to establish trust, surface competing needs, and build shared ownership with business and technical stakeholders at all levels across functions.
- Strong business-process analysis and problem-framing skills, with the ability to translate ambiguous needs into clear use cases, requirements, experiments, solution options, delivery plans, and measurable success criteria.
- Hands-on knowledge of generative AI, prompt and context engineering, retrieval-augmented generation, agents and multi-agent patterns, workflow automation, APIs, integrations, and evaluation methods for grounding, accuracy, reliability, safety, latency, and cost.
- Demonstrated experience building enterprise solutions with Microsoft 365 Copilot, Microsoft 365 Copilot Chat, Copilot Studio, Power Automate, Power Apps, SharePoint, Teams, Microsoft Graph, Dataverse, Azure AI services, or related Microsoft technologies.
- Demonstrated ability to design, build, test, debug, deploy, and maintain solutions using Python and at least one enterprise application language such as JavaScript, TypeScript, or C#, together with SQL, REST APIs, source control, cloud services, and modern software-development practices.
- Ability to design secure, governed integrations between AI agents and enterprise tools, APIs, applications, and data sources, including appropriate authentication, authorization, identity, permissions, privacy, compliance, responsible AI, and human-in-the-loop controls.
- Strong written and verbal communication skills, including the ability to explain technical concepts, architectural trade-offs, risks, and recommendations to nontechnical audiences and executive stakeholders.
- Demonstrated change-management and user-enablement skills, including training, documentation, demonstrations, community engagement, and adoption support.
- Outcome orientation with experience defining value hypotheses and measuring adoption, efficiency, quality, risk reduction, reuse, and business impact.
- Preferred: experience delivering production AI solutions, agentic workflows, retrieval-augmented generation, custom copilots, Azure AI Foundry or Azure OpenAI solutions, data pipelines, enterprise integrations, DevOps and CI/CD practices, observability, automated testing, and secure cloud application development. Experience designing, deploying, or governing Model Context Protocol (MCP) servers and familiarity with Microsoft Work IQ, Microsoft IQ, or comparable enterprise intelligence, semantic-grounding, and agent-context capabilities are also preferred.
Physical Demands
The physical requirements described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
- Sit; use hands to finger, handle or feel objects, tools, or controls.
- Stand; walk; reach with hands and arms; and stoop, kneel, crouch, or crawl.
- Other (please specify): Domestic & International travel (approx. 15%), including extended flights and work across multiple time zones.
Cynosure is an Equal Opportunity Employer – Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Disability, Veteran
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cynosure, 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. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $150K to $190K.
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.
Cynosure AI Hiring
Cynosure has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westford, MA, US. Compensation range: $190K - $190K.
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
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