Engineering Manager - MLOps (7-15 yrs)

We are seeking a highly skilled and motivated Engineering Manager - MLOps to lead and mentor our machine learning engineering team. The ideal candidate will have a strong background in software engineering, machine learning operations, and infrastructure management, combined with leadership experience.

This role is critical for ensuring the seamless development, deployment, and scaling of machine learning models into production environments.

Key Responsibilities :

Team Leadership and Management :

- Lead, mentor, and manage a team of MLOps engineers, ensuring alignment with organizational goals.

- Foster a culture of innovation, collaboration, and continuous improvement within the team.

- Set clear objectives, provide regular feedback, and support career development for team members.

MLOps Strategy and Implementation :

- Define and implement MLOps best practices, processes, and frameworks for scalable machine learning operations.

- Oversee the end-to-end lifecycle of machine learning models, including development, deployment, monitoring, and retraining.

- Collaborate with data scientists, machine learning engineers, and software developers to ensure seamless integration of ML models into production systems.

Infrastructure and Automation :

- Design and manage scalable infrastructure for ML model training, deployment, and monitoring.

- Implement CI/CD pipelines tailored for machine learning workflows to automate testing, deployment, and versioning.

- Ensure effective data pipeline orchestration, feature engineering, and data versioning using tools like Airflow, Kubeflow, or similar.

Performance Optimization and Monitoring :

- Monitor and optimize the performance of deployed models, ensuring reliability and low latency.

- Implement robust monitoring and alerting mechanisms to detect data drifts and model performance issues in real-time.

- Develop strategies for continuous retraining and improvement of ML models.

Collaboration and Stakeholder Management :

- Work closely with product managers, data scientists, and engineering teams to align ML solutions with business objectives.

- Communicate technical concepts and strategies to non-technical stakeholders, ensuring clarity and alignment.

- Collaborate with cross-functional teams to identify opportunities for machine learning applications and operational improvements.

Skills and Competencies :

Technical Skills :

- Strong programming skills in Python, Java, or similar languages.

- Expertise in MLOps tools and platforms such as Kubeflow, MLflow, Sagemaker, or Azure ML Studio.

- Experience with containerization and orchestration technologies like Docker and Kubernetes.

- Deep understanding of machine learning model development, training, and deployment pipelines.

- Proficiency in cloud platforms like AWS, GCP, or Azure.

- Strong knowledge of data engineering tools such as Apache Spark, Airflow, or Databricks.

- Experience with version control systems (i.e. Git) and CI/CD tools (i.e. Jenkins, CircleCI).

- Familiarity with monitoring tools like Prometheus, Grafana, or similar for ML model and infrastructure monitoring.

Leadership Skills :

- Proven ability to manage and grow high-performing technical teams.

- Strong problem-solving and decision-making abilities with a focus on results.

- Excellent communication and interpersonal skills for effective stakeholder management.

- Strategic mindset with the ability to prioritize and align team efforts with organizational goals.

Soft Skills :

- Analytical thinking and ability to break down complex problems.

- Attention to detail and a focus on quality.

- Strong organizational and time-management skills.

Education & Experience :

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

- 7-15 years of experience in software engineering, machine learning, or MLOps roles.

- At least 3 years of experience in a leadership or managerial capacity, overseeing MLOps or machine learning teams