Role- Lead Solution Architect

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 solid 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

- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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

Mandatory Skills : AI/ML Solution, solution architect, end to end AIML,Azure AI cloud,MLOPS,Computer vision, Kubernetes + Docker.

📌 Associate Principal - Architecture (Bengaluru)
🏢 LTM
📍 Bengaluru