MLOps Engineer - Machine Learning Models (9-14 yrs)

Job Summary:

We are seeking a highly experienced and passionate MLOps Engineer to join our growing AI/ML team. In this role, you will be responsible for building and maintaining the infrastructure and processes necessary to deploy, monitor, and scale machine learning models in production. You will work closely with data scientists and engineers to automate the ML lifecycle, ensuring the reliability, efficiency, and scalability of our AI-driven products. This is a critical role that will directly impact the success of our AI initiatives.

Responsibilities :

MLOps Infrastructure Development :

- Design and implement scalable and reliable MLOps pipelines for model training, deployment, and monitoring.

- Build and maintain infrastructure for data ingestion, feature engineering, model training, and serving.

- Automate the deployment and management of machine learning models in production environments.

- Implement robust monitoring and alerting systems to ensure the performance and stability of deployed models.

ML Lifecycle Automation :

- Develop and maintain CI/CD pipelines for machine learning models.

- Automate model versioning, testing, and deployment processes.

- Implement feature store solutions for efficient feature management and reuse.

- Streamline the process of transitioning machine learning models from research to production.

Model Monitoring and Governance :

- Implement model performance monitoring and drift detection systems.

- Develop and maintain model governance and compliance processes.

- Ensure data privacy and security in the ML lifecycle.

- Investigate and resolve production issues related to machine learning models.

Collaboration and Communication :

- Collaborate with data scientists, engineers, and product managers to define requirements and deliver solutions.

- Communicate technical concepts effectively to both technical and non-technical audiences. -

- Document MLOps processes and best practices.

- Mentor junior MLOps engineers.

Performance Optimization :

- Optimize machine learning model performance and resource utilization.

- Identify and resolve bottlenecks in the ML pipeline.

- Implement strategies for scaling machine learning models in production.

Required Technical Skills :

Cloud Platforms :

- Extensive experience with cloud platforms (AWS, Azure, or GCP) and their ML services (e.g., SageMaker, Azure ML, Vertex AI).

- Experience with containerization and orchestration tools (Docker, Kubernetes).

- Knowledge of serverless computing (e.g., Lambda, Azure Functions).

MLOps Tools and Frameworks :

- Proficiency in MLOps tools and frameworks (e.g., Kubeflow, MLflow, TensorFlow Extended (TFX), Airflow, Prefect).

- Experience with feature store solutions (e.g., Feast, Hopsworks).

- Experience with model monitoring tools (e.g., Evidently, Arize AI).

Programming and Scripting :

- Strong programming skills in Python.

- Experience with scripting languages (e.g., Bash).

- Knowledge of SQL and data manipulation libraries (e.g., Pandas, NumPy).

CI/CD and Automation :

- Experience with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, CircleCI).

- Knowledge of infrastructure as code (IaC) tools (e.g., Terraform, CloudFormation).

- Experience with version control systems (e.g., Git).

Machine Learning Concepts :

- Solid understanding of machine learning concepts and algorithms.

- Familiarity with model training and evaluation techniques.

- Knowledge of data science workflows and best practices.

Data Engineering :

- Experience with data engineering tools and techniques.

- Knowledge of data warehousing and data lake concepts.

- Experience with data pipelines and ETL processes.

Monitoring and Logging :

- Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).

- Ability to troubleshoot and resolve production issues.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Data Science, or a related field.

- 9-14 years of experience in software engineering, with a focus on MLOps or related areas.

- Proven experience in building and deploying machine learning models in production.

- Strong understanding of DevOps principles and practices.

- Excellent problem-solving and analytical skills.

- Strong communication and interpersonal skills.

- Ability to work in a fast-paced, agile environment. -

Benefits :

- Competitive salary and benefits.

- Opportunity to work on cutting-edge AI/ML projects.

- Collaborative and supportive work environment.

- Opportunities for professional growth and advancement.