Senior Machine Learning Engineer – MLOps

This is a fully remote position, open to applicants in India.

📋 Description

• Create, develop, and maintain deployment pipelines that transition models from development through validation into production.

• Implement model versioning, lineage, registry, and automated promotion methodologies.

• Establish consistent production-readiness standards and deployment practices for ML and AI workloads.

• Collaborate with Data Scientists to ensure smooth, consistent, and production-ready model handoffs.

• Take responsibility for monitoring production concerning model performance, drift, data quality, inference health, latency, and availability.

• Set up alerts and operational thresholds to detect degradation before it affects products or customers.

• Troubleshoot production failures, conduct root-cause analysis, and execute durable corrective measures.

• Develop operational practices that enhance reliability as the production model portfolio expands.

• Deploy and provide support for production LLM applications, including retrieval-based and agentic frameworks.

• Create evaluation frameworks for assessing the quality, reliability, and performance of generative AI.

• Track token consumption, inference costs, and cost-per-interaction metrics.

• Enforce controls regarding model access, usage, safety, and production behavior.

• Design and manage infrastructure for model training, validation, and retraining.

• Build automated retraining pipelines activated by performance, data, or business conditions.

• Ensure that training environments and workflows are reproducible, scalable, and observable.

• Collaborate with Data Engineering and Data Science to guarantee reliable data movement throughout the ML lifecycle.

• Implement model access controls, auditability, lineage, and governance standards.

• Assist with model risk classification and appropriate controls based on use cases.

• Generate documentation and technical evidence for security, compliance, and internal governance.

• Address incidents, troubleshoot failures, and coordinate resolutions across teams.

• Develop runbooks and operational procedures that minimize reliance on tribal knowledge.

• Identify recurring operational challenges and automate solutions where feasible.

• Work independently while collaborating with U.S.-based Data Science and Data Engineering teams.

⛳️ Requirements

• 6+ years of experience in software engineering, data engineering, machine learning engineering, or a related technical field.

• 3+ years of hands-on experience in deploying and managing AI/ML systems in a production environment.

• Proven experience supporting both traditional machine learning and LLM-based workloads in production settings.

• Strong grasp of the entire model lifecycle, including development, validation, deployment, monitoring, retraining, and retirement.

• Production experience with LLM-powered applications and agentic frameworks.

• Familiarity with retrieval architectures, evaluation methodologies, and production monitoring for generative AI.

• Understanding of LLM performance, latency, token usage, and cost-per-interaction management.

• Ability to implement practical operational and governance controls around generative AI systems.

• Extensive experience with a major cloud platform and its managed AI/ML services; AWS is highly preferred.

• Practical experience with model registries, pipeline orchestration, ML CI/CD, automated retraining, and production monitoring.

• Strong Python programming skills.

• Experience with containerization and infrastructure-as-code methodologies.

• Ability to design reliable, repeatable, and automated production environments.

• Experience managing production services with significant responsibility for reliability and availability.

• Excellent incident response, troubleshooting, and root-cause analysis capabilities.

• Ability to differentiate symptoms from underlying system failures and implement long-term fixes.

• Comfortable making sound operational decisions independently when immediate U.S.-based support is unavailable.

• Strong written communication skills for technical documentation.

• Experience generating runbooks, architectural documentation, standards, and operational procedures.

• Proactive communication style appropriate for distributed, asynchronous teams.

• Ability to collaborate effectively across Data Engineering, Data Science, Product, and other technical teams.

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

• Experience in implementing AI governance, model risk tiering, or access-control frameworks is a plus.

• Familiarity with modern data warehousing and orchestration technologies in production analytics environments is advantageous.

• Experience supporting large-scale data and ML workloads within an AWS ecosystem is a plus.

• Successful experience working in distributed global teams alongside U.S.-based colleagues is a plus.

🏝️ Benefits

• Competitive local benefits offered through our Employer of Record.

• Flexible remote working environment.

• Continuous professional development opportunities.

• Chance to collaborate directly with U.S.-based Data and Technology teams.

• Significant ownership of production systems that support Dynatron’s AI strategy.