Job Title : Senior Machine Learning Engineer (MLOps Senior Engineer)

Location : Chennai - 3 days WFO

Must Have Skills : Azure, Docker, Terraform, Vertex AI, Google Cloud Platform, CKA (Certified Kubernetes Administrator), Ansible, FastAPI, AWS, Python, Flask

Experience Level : 6 - 9 Years

Primary Skills : Python, Flask/Fast API, Docker, Kubernetes, Vertex AI, AWS, GCP, Azure.

Location : Chennai.

Shift Timing : Normal (Day Shift).

Job Description Overview :

We are seeking a highly skilled and experienced ML Ops Senior Engineer with a strong background in data engineering to join our dynamic team. The ideal candidate will be responsible for leading and implementing the deployment, monitoring, and management of machine learning models in production environments, while also possessing expertise in data engineering principles.

This role requires a deep understanding of both machine learning and data engineering concepts, as well as proficiency in software engineering and DevOps practices. The ML Ops Senior Engineer will collaborate closely with data scientists, software engineers, and DevOps professionals to optimize the end-to-end ML pipeline, ensure model scalability, and maintain high availability of ML applications.

Responsibilities :

1. Lead the design, development, and implementation of ML Ops solutions : Deploy machine learning models into production environments efficiently, leveraging data engineering best practices.

2. Collaborate with cross-functional teams : Work with data engineers, data scientists, and software engineers to integrate data pipelines with ML models (including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines) with best practices, ensuring data quality, reliability, and scalability.

3. Infrastructure management : Implement and maintain infrastructure as code.

Required Skillset:

- Demonstrated expertise in building and managing MLOps pipelines using tools such as Kubeflow, MLflow, or SageMaker, with a deep understanding of containerization technologies like Docker and Kubernetes.

- Proficiency in Python and strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn, coupled with a solid grasp of software engineering principles.

- Ability to manage cloud infrastructure on platforms like AWS, Azure, or GCP, specifically focusing on serverless computing, distributed storage, and networking.

- Strong analytical and problem-solving skills, with the ability to communicate complex technical concepts to non-technical stakeholders and collaborate effectively within a hybrid work environment in Chennai.

- A Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field, supported by 6 to 9 years of hands-on experience in production-level machine learning engineering.

- Proven capability to mentor junior engineers and drive technical initiatives that improve team productivity and code quality.