Associate Architect

Job Description

Key Skills: MLOps, Machine Learning, Distributed Systems, Python, PyTorch, TensorFlow, Kubernetes, Azure Roles and Responsibilities: Architectural Leadership: Define and own the technical roadmap and architecture for the end-to-end MLOps lifecycle -- from data ingestion and feature engineering to model deployment and monitoring. System Design: Build highly scalable, reliable, and cost-efficient distributed systems for model training, inference, and serving, including core components such as feature stores, model registries , and experiment tracking . Technical Evangelism: Promote best practices in MLOps, software design, and scalable system development. Mentor senior ML engineers to solve complex technical challenges. Innovation & R&D: Stay updated on advancements in AI/ML, Generative AI, and cloud computing (Azure/GCP) . Lead R&D initiatives to integrate new technologies that solve unique business challenges. Cross-Functional Collaboration: Partner with Data Scientists to understand modeling requirements and translate them into scalable and efficient platform capabilities. Performance & Cost Optimization: Oversee the performance, scalability, and cost management of ML workloads on the cloud, proactively resolving bottlenecks and optimizing GPU and compute utilization. Skills Required: Experience: 10+ years in software engineering, including 5+ years as a hands-on ML Engineer, and 3+ years in architecting or leading ML platform initiatives. MLOps Expertise: Hands-on experience designing and managing the full ML lifecycle in production environments. Data Engineering: Strong knowledge of distributed data processing tools ( Apache Spark, Kafka ) and feature store development. Orchestration & Deployment: Proficient with Docker, Kubernetes, CI/CD , and automated model deployment pipelines. ML Frameworks: Deep experience with PyTorch, TensorFlow, scikit-learn , and related ML libraries. Cloud Proficiency: Expertise in Microsoft Azure (preferred) or GCP , with a solid grasp of distributed systems design and cloud cost optimization. Technical Leadership: Proven ability to lead technical teams, establish architectural standards, and influence strategic decisions. Generative AI: Practical exposure to LLMs, RAG, fine-tuning, and vector databases . Domain Expertise: Prior experience in E-commerce AI/ML applications such as recommendation systems, computer vision, search ranking, or supply chain optimization is highly desirable. Education: Bachelor or Master degree in Computer Science, Engineering, or a related technical field.