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$25+ / hour
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$26 - $51 / hour
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$17+ / hour
Posted 30+ days ago

Machine Learning Architect
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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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