Developing and deploying AI systems with a focus on agentic AI, utilizing emerging protocols such as MCP and A2A.
Building and refining Generative AI systems, including RAG and LLM applications, from initial concept to production.
Rapidly creating proof-of-concepts (POCs) to demonstrate new ideas and scaling successful ones into robust software solutions.
Implementing ML Engineering and MLOps best practices for model versioning, lifecycle management, and experiment tracking to manage the entire machine learning lifecycle.
Managing data flows and model calls within live, production-level services.
Building scalable and reliable infrastructure, considering trade-offs like cost optimization, availability, and on-premises vs. cloud deployment.
Staying current with evolving tools and technologies in the AI landscape
Good programming skills, with a preference for Python.
A foundation in backend software development and system integration.
Demonstrable awareness of emerging agentic AI approaches (e.g., MCP, A2A).
An appreciation how to build GenAI systems (e.g., RAG, LLM).
Real-world experience with ML Engineering / MLOps.
Proven ability to build and deploy machine learning systems beyond the prototyping phase.
Experience with MLOps tools and workflows.
Experience with model observability and Monitoring
An understanding of infrastructure trade-offs and the ability to build scalable, reliable systems.
A proactive, impact-oriented mindset, with a strong desire to learn and grow.