Senior AI Lead Technical Ops SME Full-time

Job Description:

The Senior AI Lead Technical Ops SME plays a pivotal role in ensuring the seamless deployment, operation, and optimization of AI systems across the organization. As a technical leader, you will design and implement best practices for AI operations, troubleshoot complex issues, and guide teams in maintaining high standards for performance, scalability, and reliability. This role requires extensive experience in AI/ML operations and a deep understanding of the technical, strategic, and organizational aspects of AI systems.

Typical Responsibilities responsible for overseeing the design, deployment, and optimization of AI infrastructure and operations, ensuring seamless integration with business objectives provide technical leadership, guide best practices, troubleshoot complex issues, and collaborate with cross-functional teams to drive the success of AI projects.

Candidate Requirements Technical Skills

Deep expertise in AI/ML systems architecture, deployment, and operations. Proficient in programming languages like Python, Go, or Java. Advanced knowledge of cloud platforms (AWS, Azure, GCP) and their AI services. Expertise in containerization (Docker) and orchestration (Kubernetes). Hands-on experience with observability tools (e.g., Prometheus, Grafana, ELK Stack) and APM solutions. Strong understanding of MLOps tools and platforms MLflow, Kubeflow, SageMaker). Familiarity with distributed computing frameworks and big data systems (e.g., Apache Spark, Kafka).

Education and Experience

Bachelor or Master degree in Computer Science, Data Science, Engineering, or related fields.

Seven years of experience in AI/ML operations, technical leadership, or related roles.

Soft Skills

Exceptional problem-solving and decision-making skills. Strong leadership and mentorship capabilities. Excellent communication skills, both written and verbal, to interact with technical and non-technical stakeholders. Ability to thrive in a fast-paced, dynamic environment and manage multiple priorities. Nice-to-Have Certifications in cloud platforms (AWS Solutions Architect, Google Cloud ML Engineer, etc.Experience with AI governance frameworks and ethical AI considerations. Contributions to open-source projects or AI/ML research.