Sr Machine Learning Engineer (MLOps)

Accountabilities:

• Design, build, and maintain reliable deployment pipelines that move ML models from development and validation through production.
• Establish model versioning, lineage, registry, and automated model promotion practices.
• Define repeatable production-readiness standards and deployment patterns across machine learning and AI workloads.
• Partner with Data Scientists to make model handoffs efficient, consistent, and production-ready.
• Own production monitoring for model performance, drift, data quality, inference health, latency, availability, and other operational metrics.
• Establish alerts and operational thresholds that identify degradation before it materially impacts products or customers.
• Diagnose production failures, perform root-cause analysis, and implement durable corrective actions.
• Deploy and support production LLM applications, including retrieval-based and agentic architectures.
• Build evaluation frameworks to measure the quality, reliability, and performance of generative AI capabilities.
• Monitor token consumption, inference costs, and cost per interaction to help ensure AI workloads remain economically sustainable.
• Implement appropriate controls around model access, usage, safety, and production behavior.
• Design and operate infrastructure for model training, validation, and retraining.
• Build automated retraining pipelines triggered by relevant 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 data movement throughout the ML lifecycle.
• Implement model access controls, auditability, lineage, and governance standards.
• Support model risk classification and establish appropriate controls based on use case and business impact.
• Produce documentation and technical evidence supporting security, compliance, and internal governance requirements.
• Respond to production incidents, troubleshoot failures, and coordinate resolution across teams when required.
• Create runbooks and operational procedures that reduce reliance on undocumented knowledge.
• Identify recurring operational issues and automate them away wherever practical.
• Contribute to continuous improvements in reliability, automation, security, scalability, and operational efficiency.

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 or machine learning systems in production.
• Demonstrated experience supporting both traditional machine learning and LLM-based workloads in production environments.
• 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, generative AI evaluation methodologies, and production monitoring.
• Understanding of LLM performance, latency, token utilization, and cost-per-interaction management.
• Ability to establish practical operational, security, and governance controls around generative AI systems.
• Deep experience with a major cloud platform and its managed AI/ML services; AWS experience is 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, automated production environments.
• Proven experience operating production services with meaningful responsibility for reliability and availability.
• Strong incident response, troubleshooting, and root-cause analysis skills.
• Ability to distinguish symptoms from underlying system failures and implement sustainable solutions.
• Comfortable making sound operational decisions independently when immediate support from distributed teams may not be available.
• Strong written technical communication skills.
• Experience creating runbooks, architectural documentation, standards, and operational procedures.
• Proactive communication style suited to distributed and asynchronous teams.
• Ability to collaborate 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 with AI governance, model risk tiering, access-control frameworks, modern data warehouse technologies, or production analytics environments is a plus.
• Experience working successfully with U.S.-based colleagues and distributed global teams is advantageous.

Benefits:

• Competitive, market-aligned compensation for India.
• Full-time employment through an Employer of Record partner.
• Remote working environment within India.
• Competitive local benefits provided through the Employer of Record.
• Ongoing professional development opportunities.
• Opportunity to work directly with U.S.-based Data and Technology teams.
• Meaningful ownership of production systems supporting a growing AI strategy.
• Exposure to traditional ML, generative AI, LLMs, retrieval systems, and emerging agentic technologies.
• Opportunity to solve complex MLOps challenges involving large-scale data and real-world customer applications.
• High-impact senior individual contributor role with significant technical ownership.
• Collaboration with Data Science, Data Engineering, Product, and technology leadership.
• Opportunity to influence AI/ML engineering standards, governance, automation, and production practices.
• Autonomy and responsibility within a distributed, remote-first environment.