Lead Solution Architect Consultant

LTM

Chennai District, Bengaluru

Hybrid

INR 3,500,000 - 7,000,000

Full time

8 days ago

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Job summary

LTM in Bangalore is seeking an experienced AI/ML Solution Architect with 12-16 years to design, develop, and deploy enterprise AI/ML solutions across cloud and edge environments. You will translate complex business needs into secure, scalable architectures with a strong focus on Azure, MLOps, data architecture, APIs, and cloud-native development.

Strong hands-on technical execution is essential. You will lead end-to-end architecture for data ingestion, processing, model training, inference, and

Qualifications

12+ years of experience in AI/ML architecture and delivery.
Strong Azure, MLOps, data architecture, API design, and cloud-native development.
Proven experience taking AI/ML from PoC to production at enterprise scale.
Experience with edge AI and industrial environments is valuable.

Responsibilities

Define end-to-end architectures for enterprise AI/ML solutions.
Design computer vision & edge AI workflows.
Architect real-time video analytics using YOLO/OpenCV/CNN/transformers.
Optimize models for edge and cloud deployment.
Develop GPU-enabled inference environments with containers and Kubernetes.
Define cloud architectures for high availability, security, performance and cost.
Establish MLOps architecture, monitoring, retraining, and lifecycle management.
Lead technical discussions with stakeholders.

Skills

AI/ML solution architecture

Machine Learning

Deep Learning

Computer Vision

Predictive Analytics

Generative AI / LLM

Model optimization

AI lifecycle management

Tools

Docker

Kubernetes

Python

Git

CI/CD

Infrastructure as Code

Job description

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

AI/ML Solution Architecture
- Define end-to-end architectures for enterprise AI/ML solutions covering:
- Data ingestion
- Data processing and engineering
- Feature engineering
- Model development and training
- Model serving and inference
- Application/API integration
- 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
- Design architectures for camera edge cloud application workflows.
- Architect real-time video/image analytics solutions.
- Work with computer vision technologies such as:
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:
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.