Collaborate with cross\-functional teams to design, develop, and deploy scalable software products that incorporate machine learning and AI models.
Build reusable Python packages to support the implementation of ML/AI algorithms and data\-processing pipelines.
Contribute to the design of AI systems, including components for retrieval\-augmented generation (RAG), LLM integration, and agent\-based workflows.
Develop agentic evaluation and monitoring frameworks to assess model reasoning, consistency, and fairness.
Evaluate database design and create optimized performance queries for efficient data processing and retrieval.
Break down complex MLE and AI tasks into manageable user and technical stories, ensuring efficient and effective implementation.
Ensure high\-quality test coverage of ML code and participate in peer reviews to provide valuable recommendations.
Stay updated on the latest advances in machine learning engineering, generative AI, and AI system orchestration, and incorporate relevant practices into our workflows.
Contribute to continuous delivery and Agile development processes, adhering to best practices in ML and AI engineering.
*What Will Make You a Great Fit**
Master’s degree in computer science, software engineering, data science, or a related field (PhD preferred).
Minimum of 7 years of professional experience designing, developing, and deploying machine learning software products independently and collaboratively.
Strong programming skills in Python, with experience developing reusable packages and automation tools.
Familiarity with Databricks for scalable data processing and collaborative analytics.
Solid understanding of machine learning systems design concepts, including model lifecycle management, MLOps, and scalable inference.
Hands\-on experience with cloud infrastructure (Azure, AWS, or GCP), containerization, and CI/CD pipelines.
Proficiency with distributed computing frameworks, machine learning packages, and both relational and nonrelational databases.
Familiarity with generative AI tools and frameworks (e.g., AutoGen, Hugging Face, LangChain, LangGraph, LlamaIndex) and their integration into enterprise pipelines.
Experience developing or evaluating agentic AI systems, AI orchestration, or AI\-assisted decision\-making workflows is an asset.
Excellent problem\-solving and analytical skills, with the ability to break down complex tasks into actionable steps.