Key Responsibilities

1. AI/ML Solution Architecture

- Define end-to-end architectures for enterprise AI/ML solutions covering data ingestion, processing, feature engineering, model development, inference, application integration, monitoring, and governance.

- Translate business and operational requirements into scalable technical architectures.

- Select appropriate AI/ML frameworks, cloud services, infrastructure, and deployment patterns.

- Establish architecture standards, reference architectures, design patterns, and technology roadmaps.

- Evaluate emerging AI/ML technologies and determine their suitability for business requirements.

2.

Computer

Vision and Edge AI

- Design end-to-end camera-to-edge-to-cloud-to-application workflows.

- Architect real-time image and video analytics solutions.

- Work with YOLO, OpenCV, CNN/Transformer-based models, image classification, object detection and tracking, segmentation, OCR, and anomaly detection.

- Optimize models for cloud and edge deployment.

- Design GPU-enabled inference environments using containers and Kubernetes where appropriate.

- Integrate computer vision solutions with industrial systems, IoT platforms, APIs, dashboards, and enterprise applications.

- Address video bandwidth, latency, inference performance, scalability, and edge connectivity challenges.

3.

Azure Cloud

Architecture

- Design secure, scalable, highly available, and cost-effective Azure cloud architectures.

- Develop hybrid cloud and edge architectures for AI inference close to industrial assets and data sources.

- Define cloud infrastructure and deployment patterns that meet performance and reliability requirements.

- Support cloud-native AI/ML application development and enterprise integration.

4. MLOps and AI Lifecycle Management

- Establish enterprise-grade MLOps architecture and practices.

- Define model versioning, experiment tracking, model registry, and artifact management.

- Design monitoring for model performance, data drift, concept drift, data quality, infrastructure health, latency, and throughput.

- Enable automated retraining and model lifecycle management.

- Integrate ML pipelines with Azure DevOps, GitHub, CI/CD, and Infrastructure-as-Code practices.

5. Data Architecture

- Define data architectures supporting AI/ML workloads from ingestion to consumption.

- Design batch and real-time data pipelines.

- Establish ingestion patterns using APIs, event streams, IoT telemetry, databases, and files.

- Architect solutions for structured, semi-structured, and unstructured data.

- Ensure data architectures meet scalability, security, availability, and AI/ML requirements.

6.



API and Integration Architecture

- Design RESTful APIs and event-driven integration architectures.

- Develop architectural patterns for microservices-based AI/ML applications.

- Use Azure API Management and other integration services where appropriate.

- Integrate AI/ML services with enterprise applications, IoT/OT platforms, mobile and web applications, and Power Platform.

- Define secure and reliable application integration patterns.

7. Application and Platform Architecture

- Design cloud-native applications using microservices and containerized workloads.

- Define scalable backend services supporting AI/ML applications.

- Establish synchronous and asynchronous processing patterns.

- Define caching, messaging, database, and service-discovery strategies.

- Ensure applications support horizontal scaling and high availability.

- Guide development teams on implementation standards and architectural best practices.

8. Security and Governance

- Incorporate security by design across AI, data, API, and cloud architectures.

- Define data protection, encryption, and access control mechanisms.

- Address AI/ML governance, responsible AI, model security, and auditability.

- Ensure compliance with enterprise security and regulatory requirements.

9. Productionization and Enterprise Scale

- Assess PoCs and identify the architectural changes required for production readiness.

- Identify scalability, reliability, security, and operational gaps.

- Establish production deployment and operational support patterns.

- Design solutions that support large numbers of users, devices, cameras, industrial assets, or sites.

- Define Service Level Agreements (SLAs), Service Level Objectives (SLOs), and non-functional requirements.

- Optimize compute, storage, networking, and AI inference costs.

- Establish observability and production support models.

10.

Technical

Leadership

- Provide technical leadership to data scientists, ML engineers, software engineers, cloud engineers, and DevOps teams.

- Conduct architecture and technical design reviews.

- Mentor engineering teams on cloud-native AI/ML architecture.

- Prepare High-Level Designs (HLDs), Low-Level Designs (LLDs), architecture diagrams, Architecture Decision Records (ADRs), and technical standards.





- Collaborate with product managers, business stakeholders, cybersecurity teams, enterprise architects, and operations teams.

- Lead technical discussions with customers and senior stakeholders.

- Support technology evaluations, PoCs, technical proposals, and solution demonstrations.

Mandatory Technical Skills

AI/ML and Solution Architecture

- AI/ML solution architecture

- Machine Learning and Deep Learning

- AI solution lifecycle management

- Model optimization and inference

- Predictive analytics

Programming and Software Engineering

- Robust Python development

- C#/.NET knowledge

- Docker and Kubernetes

- Git, CI/CD, and Infrastructure as Code

Computer Vision

- YOLO and object detection

- OpenCV

- PyTorch and TensorFlow

- Image classification, object tracking, and segmentation

- Video analytics and edge inference

- GPU optimization

Cloud and MLOps

- Microsoft Azure architecture

- MLOps and production ML lifecycle management

- Cloud-native application architecture

- Data engineering and data pipelines

- Model monitoring and deployment

Integration and Enterprise Architecture

- RESTful APIs

- Microservices

- Event-driven architecture

- Data architecture

- Cloud security and enterprise architecture

- Productionization and AI at scale

Good-to-Have Experience

- Generative AI and Large Language Model (LLM) architecture.

- Azure Machine Learning and Azure IoT.

- Industrial AI and Edge AI implementations.

- Hybrid cloud and edge computing architectures.

- IoT/OT platform integrations.

- Experience with GPU-enabled inference and containerized workloads.

- Background in Oil & Gas, Energy, Manufacturing, Utilities, Mining, or other industrial environments.

- Experience building AI solutions for real-time industrial monitoring and operational analytics.

Required Professional Skills

- Strong analytical and problem-solving abilities.

- Excellent communication and stakeholder management skills.

- Ability to translate complex business problems into technical architecture.

- Strong technical leadership and decision-making skills.

- Experience mentoring engineering teams and conducting architecture reviews.

- Ability to balance scalability, security, performance, reliability, and cost.

- Experience driving AI/ML initiatives from experimentation to production.

Pay: ₹1,500,000.00 - ₹2,400,000.00 per year

Benefits

- Paid time off

Application Question(s)

- By applying, you agree that your resume and personal details may be stored securely for up to 2 years for recruitment purposes.

Work Location: Hybrid remote in Bangalore City, Bengaluru, Karnataka

📌 AI/ML Solution Architect (Bengaluru)
🏢 Ananta Services
📍 Bengaluru