Senior MLOps Engineer

Yotta Data Services Private Limited; Mumbai; Presencial; INR 4.500.000 - 7.500.000; Jornada completa

Descripción de la vacante

Yotta Data Services Private Limited seeks a Senior MLOps Engineer to own the operational backbone taking AI models from experimentation to reliable production.

You will work at the intersection of AI/ML, infrastructure, and DevOps, ensuring reproducible, observable, and secure workflows in real-world environments. Collaboration with researchers, engineers, infra, and product leaders is key.

Formación

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Engineering, or related field.
• 8+ 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.

Responsabilidades

• A. Model Deployment & Lifecycle: design and maintain pipelines for training, validation, deployment, rollback, and versioning.
• Own end-to-end ML lifecycle across experimentation to production.
• Enable safe model promotion via CI/CD for ML.
• Implement model registry and artifact management systems.
• B. MLOps Infrastructure: build platforms with MLflow, Kubeflow, Ray, Airflow; design scalable inference.
• Optimize GPU/CPU utilization; collaborate with infra on Kubernetes-based serving.
• C. Monitoring & Reliability: monitor performance, drift, data quality, latency, cost.
• Alert for degradation and infra failures.
• D. Data & Governance: dataset versioning, lineage tracking, and schema enforcement.
• Support A/B testing and canary deployments.
• E. Security & Compliance: enforce secrets/security, governance per GDPR/India DPDP, audit trails.
• F. Collaboration: productionize research outputs; align SLAs; provide reusable templates; mentor juniors.

Conocimientos

Python

MLOps

CI/CD

Cloud platforms

Educación

Bachelor’s or Master’s in CS/AI/ML related field

Herramientas

MLflow

Kubeflow

Ray

Airflow

GitHub Actions

GitLab CI

Argo

Jenkins

Docker

Kubernetes

AWS

GCP

Azure

Descripción del empleo

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.

Experience:

5–8 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.
• 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.

Qualification:

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Engineering, or related field.
• 8 plus 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.