About Dynatron

The Opportunity

What You’ll Do

Build & Own the ML Production Lifecycle

• Design, build, and maintain deployment pipelines that move models reliably from development through validation and into production.
• Establish model versioning, lineage, registry, and automated promotion practices.
• Define repeatable production-readiness standards and deployment patterns across ML and AI workloads.
• Partner with Data Scientists to make model handoffs efficient, consistent, and production-ready.

Drive ML Reliability & Observability

• Own production monitoring across model performance, drift, data quality, inference health, latency, and availability.
• Establish alerts and operational thresholds that identify degradation before it materially impacts downstream products or customers.
• Diagnose production failures, perform root-cause analysis, and implement durable corrective actions.
• Build operational practices that improve reliability as Dynatron’s portfolio of production models grows.

Operationalize Generative AI & LLM Workloads

• Deploy and support production LLM applications, including retrieval-based and agentic architectures.
• Build evaluation frameworks that measure quality, reliability, and performance of generative AI capabilities.
• Monitor token consumption, inference costs, and cost per interaction to ensure AI capabilities remain economically sustainable.
• Implement appropriate controls around model access, usage, safety, and production behavior.

Build Training & Retraining Infrastructure

• Design and operate infrastructure supporting model training, validation, and retraining.
• Build automated retraining pipelines triggered by appropriate performance, data, or business conditions.
• Ensure training environments and workflows are reproducible, scalable, and observable.
• Partner with Data Engineering and Data Science to ensure reliable movement of data throughout the ML lifecycle.

Establish AI/ML Governance

• Implement model access controls, auditability, lineage, and governance standards.
• Support model risk classification and appropriate controls based on use case and business impact.
• Produce documentation and technical evidence required to support security, compliance, and internal governance requirements.
• Help establish responsible production practices as Dynatron expands its use of AI.

Own Production Operations

• Take meaningful ownership of the operational health of Dynatron’s production ML and AI services.
• Respond to incidents, troubleshoot failures, and coordinate resolution across teams when necessary.
• Build runbooks and operational procedures that reduce dependence on tribal knowledge.
• Identify recurring operational issues and automate them away wherever practical.

What You Bring

MLOps & Production ML Experience

• 6+ years of experience in software engineering, data engineering, machine learning engineering, or a related technical discipline.
• 3+ years of hands-on experience deploying and operating AI/ML systems in production.
• Demonstrated experience supporting both traditional machine learning and LLM-based workloads in production.
• Strong understanding of the complete model lifecycle from development and validation through deployment, monitoring, retraining, and retirement.

Generative AI & LLM Expertise

• Production experience with LLM-powered applications and agentic frameworks.
• Experience with retrieval architectures, evaluation methodologies, and production monitoring for generative AI.
• Understanding of LLM performance, latency, token utilization, and cost-per-interaction management.
• Ability to establish practical operational and governance controls around generative AI systems.

Cloud & MLOps Engineering

• Deep experience with a major cloud platform and its managed AI/ML services; AWS strongly preferred.
• Hands-on experience with model registries, pipeline orchestration, ML CI/CD, automated retraining, and production monitoring.
• Strong Python engineering skills.
• Experience with containerization and infrastructure-as-code.
• Experience designing reliable, repeatable, and automated production environments.

Production Operations

• Experience operating production services with meaningful ownership for reliability and availability.
• Strong incident response, troubleshooting, and root-cause analysis skills.
• Ability to distinguish symptoms from underlying system failures and implement long-term solutions.
• Comfortable making sound operational decisions independently when immediate U.S.-based support may not be available.

Communication & Documentation

• Strong written technical communication skills.
• Experience creating runbooks, architectural documentation, standards, and operational procedures.
• Proactive communication style suited to distributed, asynchronous teams.
• Ability to work effectively across Data Engineering, Data Science, Product, and other technical functions.

Education

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience.

Nice to Have

• Experience implementing AI governance, model risk tiering, or access-control frameworks.
• Experience with modern data warehouse and orchestration technologies in production analytics environments.
• Experience supporting large-scale data and ML workloads within an AWS ecosystem.
• Experience working successfully on distributed global teams with U.S.-based colleagues.

What Success Looks Like

• Make deploying a model to production repeatable rather than exceptional.
• Build production AI and ML services that are observable, reliable, secure, and cost-effective.
• Identify model degradation and operational issues before they become significant customer or business problems.
• Create clear standards for how ML and AI capabilities move from experimentation into production.
• Build governance into the ML lifecycle rather than adding it after deployment.
• Reduce manual intervention through automation and strong engineering practices.
• Create documentation and runbooks that allow knowledge to scale across the organization.
• Operate independently while maintaining strong partnership with U.S.-based Data Science and Data Engineering teams.

Why Dynatron

• Help build the production foundation behind a rapidly expanding portfolio of AI-enabled SaaS products.
• Work across traditional ML, generative AI, LLMs, and emerging agentic technologies.
• Solve meaningful MLOps challenges against large-scale automotive datasets and real-world customer applications.
• High-impact senior IC role with meaningful ownership of Dynatron’s production AI infrastructure.
• Work closely with Data Science, Data Engineering, Product, and technology leadership as Dynatron continues its evolution toward an AI-first organization.
• Remote environment offering autonomy, ownership, and significant technical responsibility.

Compensation & Benefits

• Competitive local benefits provided through our Employer of Record
• Remote working environment
• Ongoing professional development opportunities
• Opportunity to work directly with U.S.-based Data and Technology teams
• Meaningful ownership of production systems supporting Dynatron’s AI strategy