The Role
Own the production lifecycle for traditional ML and generative AI systems, including deployment pipelines, model registries, monitoring, retraining, governance, and incident response. Build reliable AWS-based infrastructure for LLM and agentic applications, establish observability and cost controls, and create operational standards, runbooks, and documentation. Partner with U.S.-based Data Science, Data Engineering, and Product teams while independently managing production workloads during India operating hours.
Summary Generated by Built In
About DynatronDynatron is transforming the automotive service industry with intelligent SaaS solutions that drive measurable results for thousands of dealership service departments. Our analytics, automation, and AI-powered workflows help service leaders improve profitability, increase operational efficiency, and make smarter business decisions.
As Dynatron expands its AI and machine learning capabilities, building models is only part of the challenge. We need the infrastructure, engineering discipline, and operational rigor to deploy those capabilities reliably, monitor them continuously, and scale them confidently.The OpportunityWe’re looking for a Senior Machine Learning Engineer (MLOps) to own the production infrastructure and operational lifecycle behind Dynatron’s growing AI and machine learning capabilities.
This is a senior, hands-on engineering role focused on turning models into reliable production services. You’ll build and own the pipelines, infrastructure, monitoring, governance, and deployment practices that allow our Data Science team to move from experimentation to production safely and repeatedly.
You’ll support both traditional machine learning and modern generative AI/LLM workloads, including retrieval-based and agentic applications. You’ll be responsible not simply for getting models into production, but for ensuring they remain performant, observable, secure, cost-effective, and maintainable once they get there.
You’ll work closely with our U.S.-based Data Science team. Because many of Dynatron’s data pipelines and model workloads run overnight in U.S. time, they align naturally with the India workday. You will have significant ownership of the production environment during this critical operating window.What You’ll DoBuild & Own the ML Production Lifecycle
Employment: Full-time through Dynatron’s Employer of Record partner
Benefits Include:
As Dynatron expands its AI and machine learning capabilities, building models is only part of the challenge. We need the infrastructure, engineering discipline, and operational rigor to deploy those capabilities reliably, monitor them continuously, and scale them confidently.The OpportunityWe’re looking for a Senior Machine Learning Engineer (MLOps) to own the production infrastructure and operational lifecycle behind Dynatron’s growing AI and machine learning capabilities.
This is a senior, hands-on engineering role focused on turning models into reliable production services. You’ll build and own the pipelines, infrastructure, monitoring, governance, and deployment practices that allow our Data Science team to move from experimentation to production safely and repeatedly.
You’ll support both traditional machine learning and modern generative AI/LLM workloads, including retrieval-based and agentic applications. You’ll be responsible not simply for getting models into production, but for ensuring they remain performant, observable, secure, cost-effective, and maintainable once they get there.
You’ll work closely with our U.S.-based Data Science team. Because many of Dynatron’s data pipelines and model workloads run overnight in U.S. time, they align naturally with the India workday. You will have significant ownership of the production environment during this critical operating window.What You’ll DoBuild & 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Bachelor’s degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience.
- 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.
- 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.
- 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.
Employment: Full-time through Dynatron’s Employer of Record partner
Benefits Include:
- 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
Skills Required
- 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
- Production experience supporting both traditional machine learning and LLM-based workloads
- Strong understanding of the complete model lifecycle, including development, validation, deployment, monitoring, retraining, and retirement
- Production experience with LLM-powered applications and agentic frameworks
- Experience with retrieval architectures, evaluation methodologies, and generative AI production monitoring
- Understanding of LLM performance, latency, token utilization, and cost-per-interaction management
- Ability to establish operational and governance controls for generative AI systems
- Deep experience with a major cloud platform and managed AI/ML services; AWS strongly preferred
- 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
- Experience operating production services with ownership for reliability and availability
- Strong incident response, troubleshooting, and root-cause analysis skills
- Ability to make independent operational decisions in distributed teams
- Strong written technical communication skills
- Experience creating runbooks, architectural documentation, standards, and operational procedures
- Ability to work effectively across Data Engineering, Data Science, Product, and other technical functions
- Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience
- 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
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
At Dynatron Software, we help automotive service departments increase revenue and profitability with our suite of automotive fixed operations data analytics software, comparative insights, and expert coaching. Chaired by industry luminary Les Silver, Dynatron Software has over 24+ years of experience building solutions focused on improving revenue and increasing profitability. Dynatron currently has 175 employees located across the United States! ➤Our Company Mission We strive to be a people-first company where employees enjoy coming to work, the people they work with, and are given the autonomy to succeed. Our company culture is built on a foundation of teamwork, accountability, integrity, clear communication, and positive attitudes. Our experienced executive team leads by example, creating a positive work environment where feedback is straightforward and your hard work is rewarded. This approach has led Dynatron to consistent and steady growth across multiple areas year over year.
