Responsibilities
- Leadership & Strategy: Define and implement the vision for AI engineering productivity, AgentOps, and developer experience, aligning with organizational goals and industry best practices. Lead the strategic direction for MLOps and AgentOps, ensuring scalability, reliability, and efficiency.
- AgentOps & MLOps Development: Architect, develop, and maintain robust MLOps pipelines and AgentOps frameworks to automate the entire ML lifecycle, from experimentation and training to deployment and monitoring. Design and implement tools and processes for managing intelligent agents, including their lifecycle, performance, and ethical considerations.
- Tools & Platform Enhancement: Identify, evaluate, and integrate best-in-class tools and platforms (e.g., feature stores, model registries, data drift detection, prompt engineering tools) to improve developer productivity and model performance. Develop and maintain internal libraries, APIs, and services to streamline AI development and deployment.
- Developer Experience (DX) Improvement: Champion initiatives to improve the end-to-end developer journey, including documentation, onboarding processes, feedback loops, and self-service tools. Foster a culture of technical excellence, collaboration, and continuous improvement within the AI engineering team.
- Collaboration & Mentorship: Work closely with AI researchers, engineers, and product teams to understand their needs and translate them into practical solutions. Mentor junior engineers, promoting knowledge sharing and professional growth.
- Performance Monitoring & Optimization: Establish metrics and implement monitoring systems to track the performance and efficiency of AI models, pipelines, and developer workflows. Continuously identify and address bottlenecks, optimizing for speed, cost, and resource utilization.
Qualifications
Required:
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 10+ years of experience in software engineering, with at least 5 years focused on AI/ML engineering, MLOps, or related fields.
- Proven experience in designing and implementing MLOps pipelines and AgentOps frameworks.
- Expertise in cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
- Strong programming skills in Python and experience with AI/ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and version control (Git).
- Excellent leadership, communication, and interpersonal skills.
- Ability to work independently and collaboratively in a fast-paced environment.
- The successful candidate must not be subject to employment restrictions from a former employer.
Preferred:
- Master’s or Ph.D. in a relevant technical field.
- Experience with data orchestration tools (e.g., Airflow, MLflow).
- Familiarity with ethical AI principles and responsible AI development practices.
- Experience with large language models (LLMs) and their deployment.
- U.S. Citizenship (for government contracts, if applicable).
