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 Dev Ops. 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 product ionize 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
Bachelor’s or master’s degree in computer science, AI/ML, Data Engineering, or related field.
5–8 years of experience in MLOps, ML Engineering, or AI infrastructure roles.
Strong proficiency in Python and ML frameworks (PyTorch, Tensor Flow).
Hands-on experience with MLflow, Kubeflow, Ray, Airflow, or similar MLOps stacks.

Experience with CI/CD for ML (Git Hub Actions, Git Lab 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.