Sr Machine Learning Engineer (MLOps) - Remote India

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.