Sr, ML Architect

We are seeking a highly skilled Senior ML Architect with extensive experience in designing and developing machine learning algorithms and deep learning applications, particularly for observability data (AIOps). The ideal candidate will have a strong background in time series forecasting, anomaly detection, event classification, and correlation ML algorithms. Additionally, experience in integrating with large language models (LLMs) and generative AI (GenAI) for effective summarization and other applications is essential.

Key Responsibilities:

• Architect and design advanced machine learning algorithms for time series forecasting, anomaly detection, event classification, and correlation.
• Develop and implement deep learning applications and systems for observability data (AIOps).
• Integrate with large language models (LLMs) and generative AI (GenAI) using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques.
• Implement MCP client and server within the Grafana ecosystem or similar platforms.
• Collaborate with cross-functional teams to ensure seamless integration and deployment of ML models.
• Lead and mentor a team of engineers, both onshore and offshore.
• Provide strategic guidance on AI/ML best practices and emerging technologies.

Required Skills and Experience:

• Programming Languages: Proficiency in Python and R.
• ML Frameworks: Extensive experience with TensorFlow, PyTorch, and scikit-learn.
• Cloud Platforms: Working knowledge of Google Cloud and Azure.
• Design Tools: Proficiency in Figma, Adobe XD, or Sketch.
• Databases: Knowledge of MySQL, MongoDB, and PostgreSQL.
• Server-Side Languages: Familiarity with Python, Node.js, and Java.
• Version Control: Experience with Git and other version control systems.
• Testing: Knowledge of testing frameworks and methodologies.
• Agile Development: Experience with agile development methodologies.
• Communication and Collaboration: Strong communication and collaboration skills.
• GenAI Experience: Proven experience in integrating and leveraging generative AI models for various applications, including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG)