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About This Role
WHAT YOU’LL DO
Seakeeper is revolutionizing the marine industry through cutting\-edge technologies in a fast\-moving, innovation\-driven organization. As our Director of Software Engineering, DevOps \& AI, you'll lead the development of the cloud platform, building the infrastructure, engineering practices, and AI capabilities that power the next generation of the Seakeeper experience. You'll partner closely with leaders across Engineering, Product, Manufacturing, and Business Technology to deliver scalable platforms, accelerate digital innovation, and transform data into business value. You'll make an immediate impact across Seakeeper by:
Software Engineering
- Leading a team of software engineers responsible for platform services, APIs, and internal business solutions.
- Providing technical direction, architectural oversight, and engineering standards across all software initiatives
- Driving effective planning, prioritization, and execution through modern software development practices.
- Recruiting, developing, and mentoring engineering talent while building a culture of accountability, collaboration, and continuous improvement
- Balancing strategic leadership with hands\-on engagement in architecture, design reviews, and technical decision\-making
DevOps
- Establishing and maturing DevOps practices across Business Technology, including CI/CD, infrastructure\-as\-code, monitoring, and incident management
- Defining release governance, deployment standards, testing disciplines, and operational best practices
- Partnering with Engineering to align development, security, and deployment processes
- Building and leading a high\-performing DevOps capability that improves speed, stability, and reliability
- Tracking and improving key platform performance metrics, including uptime, deployment frequency, and recovery times
Cloud Architecture \& Connected Product Platform
- Establishing the platform's security, networking, identity, and trust architecture in partnership with Embedded Systems Engineering
- Ensuring scalability, reliability, and maintainability as the connected fleet and product portfolio grow
- Evaluating technology solutions and vendors, making strategic build\-versus\-buy recommendations
Applied AI \& Machine Learning
- Leading the identification and delivery of AI and machine learning solutions that improve products, operations, quality, and customer outcomes
- Developing predictive and prescriptive analytics capabilities using manufacturing, and enterprise data
- Establishing AI/ML development, deployment, governance, and lifecycle management practices
- Partnering with business and functional leaders to prioritize high\-value automation and intelligence initiatives
- Driving the practical adoption of AI technologies that create measurable business impact across the organization
WHAT YOU NEED TO SUCCEED
Do you have a positive attitude, an eagerness to learn, and the ability to hustle in a fast\-paced environment? Then Seakeeper is the place for you! Here are a few other specifics you’ll need to have to succeed.
MUST\-HAVES
- Bachelor’s degree in computer science, software engineering, electrical engineering, or a related technical field required.
- Experience working in business systems, IT, or technology leadership role with 10\+ years of progressive experience including:
+ Cloud architecture or platform engineering, with production experience on Microsoft Azure
+ Hands\-on experience with Container Apps, Azure Kubernetes Service (AKS) or equivalent container orchestration, PostgreSQL or similar relational databases, Blob Storage, Key Vault, and Entra ID
+ Direct experience shipping OTA or firmware\-update infrastructure for a physical connected product – automotive, marine, industrial IoT, medical device, or similar
+ Infrastructure\-as\-code experience with Terraform
+ Networking fundamentals — virtual network design (hub\-and\-spoke topology), VPN Gateway, TLS/mTLS, DNS, and load balancing
+ Experience designing microservices architectures, RESTful APIs, and event\-driven integration patterns
+ Hands\-on DevOps practice: CI/CD pipeline design and implementation (Azure DevOps or GitHub Actions
+ Containerization and orchestration experience (Docker, Kubernetes)
+ A dynamic leader who understands the value of being present, accessible and accountable to their team and the business stakeholders
+ Observability tooling — Azure Monitor, Log Analytics, and Application Insights, or equivalent platforms (Datadog, Grafana/Prometheus)
+ Experience with progressive delivery patterns — canary releases, blue\-green deployments, feature flags, and automated rollback
+ Applied AI/ML experience with real technical depth — Python and standard frameworks (PyTorch, scikit\-learn, or similar)
+ Demonstrated experience building custom ML models from the ground up for process\-improvement or operational use cases — not limited to configuring pre\-built APIs or AutoML platforms
+ MLOps practice — model versioning, deployment, and monitoring in production (MLflow, Azure Machine Learning, or equivalent)
+ Data pipeline and ETL/ELT experience (Azure Data Factory, dbt, or equivalent)
+ Experience with time\-series data and predictive maintenance or anomaly\-detection modeling is highly relevant given the telemetry use case
+ Security and PKI experience — certificate lifecycles, code\-signing chains, and trust architecture. This role owns the signing chain for every firmware update that reaches the fleet, not a delegated afterthought
+ Secrets management experience (Azure Key Vault or HashiCorp Vault)
+ People\-management – you've directly managed engineers before, including hiring, code review, and career development
+ Managing budgets \& external vendor relationships
- Familiarity with the following:
+ IoT/embedded security threat models and mitigations
+ Data warehousing concepts and tooling (Azure Synapse, Snowflake, or similar)
+ SRE practices — SLOs/SLIs, error budgets, on\-call rotations, and incident management
- Comfortable operating at the executive level — this role feeds directly into quarterly technology reviews with the CEO, CFO, and CTO, and needs to translate technical tradeoffs into business terms
- Comfortable being the architect and the builder at once — this is a small, growing team, not one with a bench to hand things off to
- Track record partnering directly with embedded or firmware engineering teams across the device\-to\-cloud boundary.
- Excellent communication, stakeholder management, and strategic planning skills
- Flexible and adaptable with the ability to deal with ambiguity and triaging competing priorities
- Openness to collaboration in all scenarios – you bring good ideas to the table, but can also recognize them from others
- Flexible and agile with the ability to pivot quickly to changing circumstances and business demands
- Changemaker with a bias for positive action
NICE\-TO\-HAVES
- Master’s degree in computer science, data science, or a related field preferred
- Relevant industry certifications preferred – Microsoft Certified: Azure Solutions Architect Expert, Azure AI Engineer Associate, Certified Kubernetes Administrator (CKA), or equivalent
- Infrastructure\-as\-code experience with Bicep or ARM templates
- Experience in marine, automotive, or industrial equipment connected\-product platforms
- Prior experience standing up DevOps practice from scratch at a growth\-stage or PE\-owned manufacturing company
- Experience managing external technology vendors and evaluating build\-vs\-buy tradeoffs on live platform decisions
- Contribution to open\-source tooling in the DevOps, MLOps, or IoT space
- Experience operating in a multi\-site or distributed manufacturing environment
MORE DETAILS YOU'LL WANT TO KNOW
- This is a new role that you will have the opportunity to pioneer and make an immediate impact
- This is an onsite position, based in either our Leesport, PA, or Ft. Myers, FL, locations
- Up to 25% travel to other Seakeeper facilities as well as other Seakeeper events is expected
- You’ll report to the Vice President of Business Technology and have two direct reports to start with
WHY YOU'LL LOVE IT HERE
It’s true that we make extraordinary products, but our favorite part about Seakeeper is our people! We believe in participative leadership. That means you have the freedom to make a difference and contribute to the larger goal, regardless of your position. Great ideas can strike at any moment, and when you have one, you’re empowered to speak up!
We are constantly pushing (or crushing) boundaries. Stagnant or bored are about as opposite from Seakeeper as you can get! We move quick and if there is something that can be improved upon, we jump on it.
WHO WE ARE
71% of our Earth is covered by water and we want everyone to make the most of it. That’s why we are committed to creating products that transform the boating experience with an organization that employees want to work for, customers want to buy from, and vendors want to partner with. Seakeeper Ride, a Vessel Attitude Control System, eliminates underway pitch and roll, making time on the water safer and more comfortable for everyone on board.
Seakeeper was founded in 2008, growing from a start\-up operation to the worldwide leader in stabilization thanks to its popular line of gyrostabilizers. Seakeeper Ride launched in 2022 and is the first product deviation from that line, bringing the magic of Seakeeper to boats while underway. There’s a long runway of growth ahead as more and more OEMs adopt Seakeeper Ride as standard equipment and we work to make aftermarket refits a reality.
Ready for a new challenge in a fast\-paced environment? Want to help us disrupt an industry? Come on and apply, we are ready for you!
Sign up to receive email updates about Seakeeper’s current open job opportunities: www.seakeeper.com/job\-sign\-up
Seakeeper is personally committed to building an inclusive and diverse workforce. We are an Equal Employment Opportunity Employer/Affirmative Action Employer and do not discriminate on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, marital status, veteran status, genetic information, or any other protected characteristic under applicable law. All employment is decided on the basis of job requirements, individual qualifications, and business need.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Seakeeper, Inc, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Seakeeper, Inc AI Hiring
Seakeeper, Inc has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Fort Myers, FL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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