AI Solutions Architect

We are seeking a highly skilled and proactive 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.

• Monitoring & Sustenance:
  • 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 Optimization:
  • 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 Collaboration:
  • 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 Management:
  • 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.
  Requirements:
  • 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 sustenance of 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.
  Preferred Qualifications:
  • 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.
  Key Competencies:
  • 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