Sciemo

Software Engineer

• 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.

Technical Staff Member

Chief of Staff

• Driving cross-functional projects across product, data science, engineering, and go-to-market
• Owning the internal operating rhythm including team syncs, OKR tracking, and decision documentation
• Leading administration functions across People, Finance and Legal
• Identifying operational gaps before they become blockers for growth

Data Engineer

• 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.

Technical Staff Member

Chief of Staff

AI/ML Engineer, Data Scientist