Head of Software Engineering, DevOps, & AI

Seakeeper Inc•Fort Myers, FL

1d•Onsite

About The Position

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

Requirements

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
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

Responsibilities

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
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
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
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