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.

AI/ML Engineer, Data Scientist

Key skills