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.

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.

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