AI Systems Reliability Engineer

We are seeking a highly skilled and proactive AI Solutions SRE Lead to oversee the maintenance, optimization, and ongoing performance of deployed AI/ML systems and solutions.

In this role, you'll act as the bridge between innovation and operations, ensuring our AI solutions consistently deliver value and operate seamlessly in real-world environments. You will lead efforts to monitor deployments, troubleshoot issues, and define best practices for sustaining AI systems throughout their lifecycle.

Key Responsibilities

• Maintenance & Optimization: Lead the post-deployment lifecycle of AI solutions, ensuring continued functionality, reliability, and scalability.
• Establish monitoring frameworks to oversee system performance, usage, and metrics for AI/ML models and APIs.
• Detect anomalies in AI systems, troubleshoot operational issues, and initiate timely corrective actions.

Performance Enhancement:

• Continuously assess and optimize the performance of AI models to maintain efficiency and accuracy in production environments.
• Collaborate with data scientists and engineers to refine algorithms, retrain models, and update solutions as needed.
• Implement automation where possible to streamline maintenance processes.

Stakeholder Engagement:

• Work with cross-functional teams (engineering, product, operations, etc.) to ensure alignment of AI sustainment activities with business goals.
• Communicate effectively with stakeholders to provide updates on system health, risks, and improvements.

Governance & Best Practices:

• Define and implement best practices for sustaining AI solutions, including documentation, testing protocols, and version control.
• Ensure compliance with ethical AI standards, regulatory guidelines, and established governance frameworks.
• Manage and mitigate risks associated with model drift, data shifts, and system vulnerabilities.

Incident Response:

• Lead responses to critical incidents involving AI systems by performing root cause analysis and deploying solutions for quick resolution.
• Advocate for proactive risk prevention and early detection strategies.
• Mentor and develop junior team members, fostering their skills in AI observability and domain-specific knowledge in ML, Computer Vision, and Generative AI.

• Bachelor's degree in Computer Science, Engineering, Data Science, or related field; advanced degree preferred.
• 9+ years of experience in machine learning, data science, or software engineering roles, with significant exposure to Computer Vision and Generative AI projects.
• 4+ years of experience specifically focused on AI/ML development and sustain the applications / solutions.
• Strong programming skills in languages such as Python, Java, or Go.
• Extensive experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and cloud platforms (e.g., AWS, Azure, GCP).
• Proficiency in data visualization tools and techniques (e.g., Grafana, Tableau, D3.js).
• Deep understanding of AI/ML concepts, including model training, evaluation, and deployment, with specific knowledge of Computer Vision and Generative AI techniques.
• Experience with monitoring and observability tools such as Prometheus, ELK stack, or similar systems.
• Excellent problem-solving skills and ability to troubleshoot complex AI systems across various domains.
• Proven track record of mentoring and developing junior team members in AI-related roles.

• Experience with MLOps practices and tools, particularly for large-scale AI systems.
• Familiarity with AI ethics and responsible AI principles, especially as they relate to Generative AI.
• Knowledge of relevant AI regulations and compliance requirements, including those specific to Computer Vision applications.
• Experience with distributed systems and large-scale data processing for AI applications.
• Contributions to open-source projects or research publications in AI solution at production scale. Previous experience with large-scale AI/ML solutions in production environments.
• Knowledge of DevOps principles and CI/CD pipelines specific to AI/ML systems.

• Strong analytical and critical thinking skills
• Excellent communication and collaboration abilities
• Proactive and self-motivated work ethic
• Ability to explain complex technical concepts to both technical and non-technical audiences
• Adaptability and willingness to learn in a rapidly evolving field
• Strong mentorship and leadership skills
• Deep curiosity and passion for AI, particularly in ML, Computer Vision, and Generative AI domains