Concurrency is looking for a Machine Learning Architect to help take their Product Engineering team to the next level by designing and implementing data-intensive ML applications in Azure that solve customer problems.
Requirements
- 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
- Experience with API development frameworks (FastAPI, Django REST framework) to support scalable data services
Responsibilities
- 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 end-to-end machine learning pipelines across supervised, unsupervised, and deep learning paradigms, with strengths in inferential statistics, time series, computer vision, or LLMs
Other
- 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
- 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
