ML Ops Data Architect

We are seeking a highly skilled and motivated MLOps Data Architect with expertise in small language models, Azure Fabric, Data Governance, AI Model Evaluation, and Building AI Agents. The ideal candidate will have a strong background in computer science, software development, and cloud infrastructure. This role involves designing, implementing, and maintaining scalable and secure cloud infrastructure, as well as collaborating with cross-functional teams to drive innovation in AI and machine learning.

Key Responsibilities:

• Develop and optimize data pipelines for small language models and AI agents.

• Ensure data governance and compliance with industry standards.

• Evaluate AI models and implement best practices for model monitoring and maintenance.

• Develop and optimize complex data pipelines, applying machine learning engineering principles to enhance efficiency and scalability.

• Employ experimental methodologies, statistics, and machine learning concepts to create self-running AI systems for predictive modeling.

• Collaborate with data science teams to review model-ready datasets and feature documentation, ensuring completeness and accuracy.

• Perform data discovery and analysis of raw data sources, applying business context to meet model development needs.

• Comfort with exploratory data exploration and tracking data lineage during inception or root cause analysis.

• Engage with internal stakeholders to understand business processes and translate requirements into analytical approaches.

• Write and maintain model monitoring scripts, diagnosing issues and coordinating resolutions based on alerts.

• Serve as a domain expert in machine learning engineering on cross-functional teams for significant initiatives.

• Stay updated with the latest advancements in AI/ML and apply them to real-world challenges.

• Participate in special projects and additional duties as assigned.

Qualifications:

• Undergraduate degree or equivalent experience; a graduate degree is preferred.

• Minimum of 5 years of relevant work experience.

• At least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g., Glue, SageMaker).

• Proficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks.

• Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.

• Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.

• Experience with API design and development is a plus.

• Solid understanding of software engineering principles, including design patterns, testing, security, and version control.

• Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.

• Understanding of solution architecture for building end-to-end machine learning data pipelines.