Machine Learning DevOps Engineer (MLOps)

Company Description

TalentXO is a Recruitment-Tech Platform, partnered with leading product companies like OLA, Razorpay, Myntra, and Deloitte for their hiring needs.

Role & Responsibilities

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate

• Strong MLOps profile
• Mandatory (Experience 1) - Must have 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• Mandatory (Experience 2) - Must have 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• Mandatory (Experience 3) - Must have 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Mandatory (Experience 4) - Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Mandatory (Experience 5) - Must have hands-on Python for pipeline & automation development
• Mandatory (Experience 6) - Must have 4+ years of experience in AWS cloud, with recent companies
• Mandatory (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth