We are looking for an experienced Machine Learning Engineer to build, deploy, and operate scalable production ML systems. The role focuses on MLOps, ML serving, distributed systems, cloud infrastructure, and production-grade ML pipelines.

Your Future Employer :

A leading global AI and analytics organization working across advanced analytics, data engineering, MLOps, Generative AI, and AI-powered solutions.

Responsibilities :

- Design, build, and operate production ML pipelines covering feature engineering, model training, serving, monitoring, and retraining.

- Build and maintain scalable, low-latency ML model serving infrastructure for real-time and batch inference.

- Own ML CI/CD processes including model versioning, automated retraining, deployments, canary releases, and rollback strategies.

- Build robust batch and streaming feature pipelines using technologies such as Spark and Kafka.

- Implement observability, data-quality checks, drift detection, SLA monitoring, and alerting for ML systems.

- Work closely with Data Scientists and ML Specialists to productionize research prototypes into scalable and maintainable services.

- Optimize ML infrastructure for performance and cost, including GPU utilization, batching, caching, and model optimization.

- Contribute to architecture and platform decisions for MLOps tooling, ML infrastructure, and distributed systems.

- Participate in production support, incident response, postmortems, and reliability improvements.

- Mentor engineers and lead technical design and architecture reviews for ML platform and serving components.

Requirements :

- 6 - 9 years of software engineering experience with 4+ years of experience building and operating production ML systems.

- Strong hands-on expertise in Java, Scala, or Python with good knowledge of software engineering practices, testing, design patterns, and code quality.

- Strong experience with AWS cloud services including EC2, EKS/Kubernetes, S3, Lambda, and IAM.

- Experience with Kafka, Spark, distributed systems, event-driven pipelines, and scalable microservices.

- Hands-on experience with ML serving frameworks such as TorchServe, TensorFlow Serving, Triton, or custom/gRPC-based services.

- Experience with MLOps and CI/CD tools such as MLflow, SageMaker Pipelines, Airflow, Jenkins, GitHub Actions, or similar platforms.

- Strong understanding of data validation, unit/integration testing, model testing, and prevention of train/serve skew.

- Experience with Terraform or similar infrastructure-as-code tools, Docker, and Kubernetes.

- Knowledge of ML fundamentals and the ability to collaborate effectively with Data Scientists and ML teams.

- Experience owning production systems, including SLAs, on-call support, incident management, and capacity planning.

What is in it for you :

- Opportunity to work on large-scale production ML and MLOps systems.

- Exposure to advanced AI, cloud, distributed systems, and Generative AI technologies.

- Opportunity to work with experienced Data Scientists, ML Engineers, and technology professionals.

- Scope to contribute to architecture, platform engineering, and high-impact ML initiatives.

- Opportunities for technical leadership, mentoring, and continuous learning.

Note :

We receive a lot of applications on a daily basis, so it becomes difficult for us to get back to each candidate. Please assume that your profile has not been shortlisted in case you don't hear back from us within 1 week. Your patience is highly appreciated.

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