Full-Time AI Data Engineering Manager

• AI System Architecture & Delivery: Lead the design and implementation of end-to-end AI systems, including GenAI solutions, RAG pipelines, and agentic workflows.
• Architect reasoning frameworks and LLM-based automation.
• Integrate AI frameworks such as LangChain, LlamaIndex, CrewAI, and LangGraph.
• Ensure high-quality, maintainable, and scalable codebases using Python.
• Build and maintain robust evaluation pipelines.
• Define and enforce AI governance strategies.

• Engineering Excellence & MLOps: Apply MLOps and software engineering best practices.
• Ensure robust CI/CD pipelines, testing, and observability across cloud platforms (AWS, Azure, GCP).
• Oversee the development of APIs and pipelines for batch and real-time inference.
• Maintain high standards of code quality, architecture, and documentation.

• People Management & Technical Leadership: Manage and grow a team of engineers.
• Provide regular coaching and mentorship.
• Facilitate effective feedback.
• Lead project planning, scope definition, and resource allocation.
• Coordinate delivery timelines, dependencies, and milestones.
• Own client relationships.

• Cross-Functional Collaboration & Communication: Work closely with product managers, data scientists, and stakeholders.
• Communicate complex technical topics to non-technical stakeholders clearly and confidently.
• Contribute to organizational AI strategy, hiring decisions, and roadmap planning.
• Excellent English communication skills (written and verbal) are essential.

• 7+ years of experience in AI/ML engineering, including at least 2 years in a team or people management role.
• Proven track record of building and deploying LLM-powered or GenAI systems in production.
• Strong proficiency in Python and familiarity with orchestration libraries like LangChain, CrewAI, and LangGraph.
• Deep understanding of agentic architectures, RAG systems, and prompt engineering.
• Experience managing engineering teams and delivering multiple concurrent projects on time.
• Hands-on experience with CI/CD, MLOps practices, and cloud platforms (AWS, Azure, or GCP).

• Experience with Snowflake, Snowpark Container Services and Snowflake Cortex.
• Background in LLM evaluation frameworks and safety practices.
• Prior ownership of client relationships or consulting engagements.

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