Associate Principal - Architecture

Role- Lead Solution Architect Location- Bangalore Experience- 12- 16years We are looking for an experienced AI/ML Solution Architect with 12+ years of experience in designing, developing, and deploying enterprise-scale AI/ML solutions across cloud and edge environments. !-- [if !supportLineBreakNewLine]--> !--[endif]--> The candidate will be responsible for translating complex business and operational problems into secure, scalable, resilient, and production-ready AI/ML architectures, with a strong focus on Microsoft Azure, MLOps, data architecture, API design, and cloud-native application development. The ideal candidate will have a strong combination of solution architecture expertise and hands-on technical experience, with demonstrated success in taking AI/ML solutions from PoC → pilot → production → enterprise scale. !-- [if !supportLineBreakNewLine]--> !--[endif]--> Experience with Computer Vision, Generative AI, predictive analytics, industrial AI, IoT/OT, or edge AI will be highly valuable, particularly within Oil & Gas, Energy, Manufacturing, or other industrial environments. Key Responsibilities 1. AI/ML Solution Architecture • Define end-to-end architectures for enterprise AI/ML solutions covering: o Data ingestion o Data processing and engineering o Feature engineering o Model development and training o Model serving and inference o Application/API integration o Monitoring and governance • Translate business and operational requirements into scalable technical architectures. • Select appropriate AI/ML frameworks, cloud services, infrastructure, and deployment patterns. • Define architecture standards, reference architectures, design patterns, and technology roadmaps. • Evaluate emerging AI/ML technologies and assess their applicability to business problems. 2. Computer Vision & Edge AI For computer-vision-driven solutions, the architect will: • Design architectures for camera → edge → cloud → application workflows. • Architect real-time video/image analytics solutions. • Work with computer vision technologies such as: o YOLO and object detection models o OpenCV o CNN/Transformer-based models o Image classification o Object detection and tracking o Segmentation o OCR o Anomaly detection • Optimize models for edge and cloud deployment. • Design GPU-enabled inference environments using containers and Kubernetes where appropriate. • Integrate vision solutions with industrial systems, IoT platforms, APIs, dashboards, and enterprise applications. • Address challenges around video bandwidth, latency, inference performance, scalability, and edge connectivity. 3. Azure Cloud Architecture • Define cloud architectures for high availability, scalability, security, performance, and cost optimization. • Design hybrid cloud + edge architectures where AI inference needs to operate close to industrial assets or data sources. 4. MLOps & AI Lifecycle Management • Establish enterprise-grade MLOps architecture and practices. • Define model versioning, experiment tracking, model registry, and artifact management. • Establish model monitoring covering: o Model performance o Data drift o Concept drift o Data quality o Infrastructure health o Latency and throughput • Enable automated retraining and model lifecycle management. • Integrate ML pipelines with Azure DevOps/GitHub and infrastructure-as-code practices. 5. Data Architecture • Define data architecture supporting AI/ML workloads from ingestion through consumption. • Architect batch and real-time data pipelines. • Define data ingestion patterns using APIs, event streams, IoT telemetry, databases, and files. • Design architectures for structured, semi-structured, and unstructured data. • Ensure data architecture supports scalability, security, availability, and AI/ML requirements. 6. API & Integration Architecture • Design RESTful APIs and event-driven integration architectures. • Design microservices-based architectures for AI/ML applications. • Use Azure API Management and other integration services where appropriate. • Integrate AI/ML services with enterprise applications, IoT/OT platforms, mobile/web applications, and Power Platform. 7. Application & Platform Architecture • Define cloud-native application architectures using microservices and containerized workloads. • Design scalable backend services supporting AI/ML applications. • Establish patterns for synchronous and asynchronous processing. • Define caching, messaging, database, and service-discovery strategies. • Ensure architecture supports horizontal scaling and high availability. • Guide development teams on implementation of architectural patterns and engineering standards. 8. Security & Governance • Incorporate security by design across AI, data, API, and cloud architectures. • Establish data protection and encryption mechanisms. • Address AI/ML governance, responsible AI, model security, and auditability. • Ensure solutions comply with enterprise security and regulatory requirements. 9. Productionization & Scale A key responsibility will be taking AI/ML solutions beyond PoC. • Assess PoCs and define the architecture required for production. • Identify scalability, reliability, security, and operational gaps. • Establish production deployment patterns. • Design solutions capable of supporting large numbers of users, devices, cameras, assets, or sites. • Define SLAs/SLOs and non-functional requirements. • Optimize compute, storage, networking, and AI inference costs. • Establish observability and operational support models. 10. Technical Leadership • Provide technical leadership to data scientists, ML engineers, software engineers, cloud engineers, and DevOps teams. • Conduct architecture reviews and technical design reviews. • Mentor engineering teams on cloud-native AI/ML architecture. • Create architecture documentation, HLDs, LLDs, diagrams, ADRs, and technical standards. • Work closely with product managers, business stakeholders, cybersecurity, enterprise architecture, and operations teams. • Lead technical discussions with customers and senior stakeholders. • Support technology evaluation, PoCs, technical proposals, and solution demonstrations. Key Skills & Technical Expertise- Core AI/ML • AI/ML solution architecture • Machine Learning • Deep Learning • Computer Vision • Predictive Analytics • Generative AI / LLM architecture • Model optimization and inference • AI solution lifecycle management Software Engineering • Strong Python development • C#/.NET • Docker • Kubernetes • Git • CI/CD • Infrastructure as Code Computer Vision • YOLO • OpenCV • PyTorch • TensorFlow • Object detection • Image classification • Object tracking • Segmentation • Video analytics • Edge inference • GPU optimization 
 Industrial / OT Experience — Preferred Experience in Oil & Gas, Energy, Manufacturing, Utilities, Mining, or other industrial environments is strongly desirable. Key competency keywords for recruitment AI/ML Architecture | Azure Cloud | MLOps | Computer Vision | Edge AI | Data Architecture | API Architecture | Microservices | Event-Driven Architecture | Kubernetes | Azure ML | Python | Docker | CI/CD | Data Engineering | Azure IoT | Generative AI | Cloud Security | Enterprise Architecture | Productionization | AI at Scale 
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