Senior MLops Engineer

Job Scope:

As a Senior MLOps Engineer, you will own the operational backbone that takes AI models from experimentation to reliable, scalable, and cost-efficient production.

This role sits at the intersection of AI/ML, infrastructure, and DevOps. You will ensure models are reproducible, observable, secure, and continuously improving in real-world environments. You will work closely with AI researchers, ML engineers, infrastructure teams, and product leaders to operationalize AI at enterprise scale workflows.

Total /Relevant Experience

7+ years of relevant experience in MLOps, ML Engineering, or AI Platform roles.

Key Responsibilities:

A. Model Deployment & Lifecycle Management

• Design and maintain robust pipelines for model training, validation, deployment, rollback, and versioning.
• Own end-to-end model lifecycle management across experimentation, staging, and production.
• Enable safe and repeatable promotion of models using CI/CD practices.
• Implement model registry and artifact management systems.

B. MLOps Infrastructure & Tooling

• Build and manage MLOps platforms using tools such as MLflow, Kubeflow, Ray, Airflow, or equivalent.
• Design scalable inference architectures for batch and real-time serving (REST, gRPC).
• Optimize GPU/CPU utilization for training and inference workloads.
• Collaborate with infra teams on Kubernetes-based model serving and orchestration.

C. Monitoring, Observability & Reliability

• Implement monitoring for model performance, drift, data quality, latency, and cost.
• Build alerting systems for model degradation and infrastructure failures.
• Enable explainability, logging, and traceability for AI outputs where required.
• Perform root-cause analysis for model or pipeline failures.

D. Data & Experimentation Pipelines

• Design reproducible data pipelines for training, validation, and inference.
• Ensure dataset versioning, lineage tracking, and schema enforcement.
• Support A/B testing, canary deployments, and controlled model experiments.
• Integrate feedback loops from production back into retraining workflows.

E. Security, Compliance & Governance

• Enforce security best practices for model access, secrets, and credentials.
• Ensure compliance with data privacy and AI governance standards (GDPR, SOC2, India DPDP Act).
• Build audit trails for model decisions in regulated or sensitive use cases.
• Partner with legal, security, and compliance teams on AI governance frameworks.

F. Cross-Functional Collaboration & Enablement

• Work closely with AI/ML engineers to productionize research outputs.
• Collaborate with Product Managers to align model SLAs with business expectations.
• Enable developers and internal teams with reusable MLOps templates and tooling.
• Mentor junior MLOps or ML engineers through code reviews and best practices.

Good-to-Have Skills

• Familiarity with LLM serving, embeddings, RAG pipelines, and vector databases.
• Knowledge of feature stores, experiment tracking, and model registries.
• Exposure to cost optimization strategies for large-scale ML systems.
• Experience working in AI-first SaaS or platform companies.

Qualifications Criteria

• Bachelors or masters degree in computer science, AI/ML, Data Engineering, or related field.
• 58 years of experience in MLOps, ML Engineering, or AI infrastructure roles.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Hands-on experience with MLflow, Kubeflow, Ray, Airflow, or similar MLOps stacks.
• Experience with CI/CD for ML (GitHub Actions, GitLab CI, Argo, Jenkins).
• Strong experience deploying models on Kubernetes with GPU workloads
• Solid experience with Docker, Kubernetes, cloud platforms (AWS/GCP/Azure).
• Proven track record of deploying and maintaining AI models in production.
• Experience supporting AI systems at scale with real users and SLAs.

Certification Criteria:

Relevant certifications in cloud platforms, MLOps, or machine learning are a plus but not mandatory.