e-source - AI / ML Engineer

• 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. • You’re likely a great fit if you: • 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. • Strong communication and collaboration skills, with a track record of working effectively in cross-functional teams. • Knowledge or experience in the utility, power, or energy sectors is a plus. • Deep knowledge in Databricks tech stack for AI and data engineering is a plus.·