Architect, build, and deploy ML/GenAI products on cloud infrastructure (AWS or similar).
Design and implement end-to-end AI workflows: data ingestion, feature engineering, modeling, evaluation, and deployment.
Create automated pipelines for continuous learning, model promotion, and performance monitoring.
Lead the design of ML orchestration frameworks (Airflow, Kedro, ZenML, Flyte) to ensure reproducibility and scalability.
Oversee deployment of large-scale and multi-agent AI systems with high reliability and fault tolerance.
Continuously optimize workflows for efficiency, robustness, and performance in production.
Translate complex business problems into AI solutions, including data collection, experiment design, and roadmap planning.
Develop interpretable, modular, and scalable ML systems that deliver measurable business value.
Work directly with customers and stakeholders to ensure deployed systems achieve their intended impact.
Stay current with advancements in AI/ML, including LLMs, diffusion models, graph AI, and agent architectures.
Propose and prototype new approaches for integrating emerging technologies into production products.
Develop methods to quantify and communicate AI performance and business ROI.
Promote responsible, ethical, and impactful AI practices across the organization.

Proven track record of launching AI/ML products into production.
Experience with core ML/AI tools: Python, PyTorch, TensorFlow / Keras, scikit-learn, SQL, Spark.
Experience writing production-grade Python (object
and function-oriented).
Hands-on expertise with large-scale ML systems, GenAI (LLMs, diffusion), agents, and graph-based models.
Experience designing and managing ML orchestration workflows and versioned pipelines (Airflow, ZenML, Kedro, dbt, etc.).
Strong problem-solving skills, adaptability, and a “hacker” mentality.
Excellent communication skills—able to work with both technical and non-technical stakeholders.
Demonstrated thought leadership and innovation in applied AI.

Technical Staff Member

Key skills