We are seeking an experienced, leadership-driven MLOps Engineer to lead our machine learning operations initiatives and maintain the highest standards of model deployment, scalability, and lifecycle management. In this senior role, you will oversee the integration of machine learning models into production environments, ensuring robust monitoring, automated retraining, and seamless CI/CD/CT pipelines. If you have strong team leadership skills, deep knowledge of MLOps best practices, and a passion for operational excellence and robust AI infrastructure, this is an outstanding opportunity to shape the technical agility and production-readiness of our AI-driven organization.

Key Responsibilities
Team Leadership: Manage, mentor, motivate, and evaluate the MLOps team, including machine learning engineers, data engineers, and infrastructure specialists.

Standards & Quality Control: Establish and enforce high standards for model versioning, automated testing, and deployment patterns; conduct regular architecture audits and post-mortem reviews of model failures.

Operations Planning: Develop and manage ML pipeline roadmaps, model serving strategies, and infrastructure plans to ensure reliable delivery of AI models at scale.

Resource & Budget Management: Control cloud compute expenditure for model training and inference, tool licensing, and infrastructure utility budgets; manage relationships with AI platform providers.

Stakeholder Collaboration: Collaborate with Data Science, Software Engineering, and Security teams to align MLOps strategies with product roadmaps and enterprise performance goals.

Training & Development: Design and deliver comprehensive training programs on MLOps tooling (e.g., Kubeflow, MLflow), best practices, and automated monitoring; mentor team leads.

Compliance & Reliability: Ensure full adherence to data privacy regulations, model governance requirements, and internal corporate infrastructure and AI security guidelines.

Reporting & Continuous Improvement: Prepare reports on model performance, pipeline health, and ROI; implement innovative AI-native methodologies and infrastructure automation to streamline the ML lifecycle.

Requirements & Qualifications
Education: Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field is preferred. Relevant MLOps or cloud certifications are highly desirable.

Experience: 5–10+ years in machine learning engineering, DevOps, or platform engineering, with at least 3–5 years in a leadership or senior managerial capacity. Experience in architecting enterprise-scale ML pipelines is highly desirable.

Technical & Management Skills:

Expertise in MLOps platforms (e.g., Kubeflow, MLflow, SageMaker, Vertex AI), container orchestration (Kubernetes), and infrastructure-as-code (Terraform).

Strong knowledge of model serving architectures, feature stores, and CI/CD/CT (Continuous Training) methodologies.

Proven ability to lead high-performing technical teams, manage complex ML deployments, and optimize total cost of ownership (TCO) for AI infrastructure.

Soft Skills:

Excellent leadership, communication, and interpersonal skills.

Strong problem-solving, decision-making, and organizational abilities.

High attention to detail with a focus on system integrity and model reliability.

Ability to work under pressure and manage multiple high-stakes deployment priorities in a dynamic environment.

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