Posted today · be early

Sr Machine Learning Engineer (MLOps) - Remote India

Dynatron Software

IndiaremotePosted today

Skill Required

MLOps-EngineeringMachine-Learning-EngineeringSenior-ML-EngineerAI-Infrastructure-EngineeringProduction-ML-EngineeringRemote-Machine-Learning-EngineerSenior-MLOps-EngineerSenior-AI-ML-Operations-EngineerMachine Learning EngineerMachine Learningsoftware engineeringSOCdata engineeringDockertroubleshootingGenerative AIObservabilityEngineeringautomationdesigninganalyticssecuritywrittenPythondesignCI/CDCloudAWSandCICDAIFulltime

Key highlights

6+ years of experience in software or data engineering
3+ years of hands-on experience deploying AI/ML in production
AWS strongly preferred
Remote working environment

Role overview

Responsibilities

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.

Requirements

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.

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.