Job description

What You'll Do - Lead and Architect - Confidently drive every stage of the ML software development lifecycle, from initial concept to full-scale production deployment - Lead workshops to gather technical and business requirements, translating customer pain points into actionable AI and data science strategies - Advise internal and external stakeholders on trends in Data & AI, influencing both technical direction and strategic initiatives to scale the company's ML market share - Architect and implement robust, scalable, and data-driven machine learning applications within Azure, balancing business value with technical innovation - Produce thought leadership through GitHub contributions, blog posts, or technical talks on LinkedIn or YouTube to elevate both personal and company profiles - Build - Build end-to-end machine learning pipelines across supervised, unsupervised, and deep learning paradigms, with strengths in inferential statistics, time series, computer vision, or LLMs. - Engineer ML solutions that integrate seamlessly with Azure cloud infrastructure, ensuring performance, scalability, and maintainability. - Utilize state-of-the-art DevOps and MLOps practices (CI/CD pipelines, containerization, and automated governance) to build production-ready systems. - Collaborate and Persuade - Serve as the bridge between sales, leadership, and customers, identifying client pain points and translating them into tailored, profitable ML solutions - Communicate complex technical architectures and ML solutions to diverse audiences, simplifying concepts for non-technical stakeholders - Navigate ambiguity in customer goals and evolving technical landscapes, while driving towards clear, measurable outcomes - Mentor and Cultivate - Be a mentor and role model for team members, fostering a high-performance MLE culture - Foster psychological safety where team members are encouraged to challenge the status quo, propose new approaches, and fail productively - Help grow and shape a technical team capable of delivering high-impact ML projects at scale What You'll Need - Technical Expertise: - Languages/Frameworks: Python, working knowledge of Scala, Java, or PySpark. Pandas, numpy, sklearn, LangChain - Azure: Deep knowledge of Azure's ecosystem including Azure ML, Data Factory, Databricks, Data Lake Storage, Cosmos DB, and Azure SQL DB. - System Design: Expertise in designing Azure architecture following Domain-Driven Design principles - AI & ML Skills: Proficiency in supervised, unsupervised, and deep learning models, including hands-on experience with LLM architectures, time series forecasting, and computer vision solutions - MLOps & DevOps: Strong understanding of MLOps pipelines and MLFlow, CI/CD automation with Azure DevOps or GitHub Actions, and containerization technologies like Docker and Kubernetes - Tools: Proficient in GitHub, PowerShell, Azure CLI, and infrastructure-as-code tools such as ARM templates or Terraform - Soft Skills & Leadership: - Proven experience leading high-performance teams in a fast-paced, customer-centric environment - Strong ability to communicate technical concepts to non-technical stakeholders, and to translate business objectives into scalable ML systems - Ability to mentor and grow teams, setting high standards for both technical quality and engineering discipline - Experience: - 10+ years of experience in system design, ML/AI architecture, and enterprise data infrastructure - Demonstrable experience building ML applications in industries such as Manufacturing, Retail, Financial Services, and Healthcare - Hands-on experience with LLM architectures, including Retrieval-Augmented Generation (RAG), single and multi-agent systems, and custom router solutions - Experience with API development frameworks (FastAPI, Django REST framework) to support scalable data services What Will Set You Apart - Microsoft MVP. - Strong software engineering experience. - Experience in professional services organizations. - Demonstrable thought leadership through an active GitHub, blog posts, or publications. - LLM experience: RAG, single and multi-agent, custom routers. - Azure Fabric implementation. - Scala, Java, Terraform.

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