Role description

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 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

Skills

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

About LTM
LTM is an AI-centric global technology services company and the Business Creativity partner to the world’s largest and most disruptive enterprises. We bring human insights and intelligent systems together to help clients create greater value at the intersection of technology and domain expertise. Our capabilities span integrated operations, transformation, and business AI — enabling new ways of working, new productivity paradigms, and recent roads to value. Together with over 87,000 employees across 40 countries and our global network of partners, LTM — a Larsen & Toubro company — owns business outcomes for our clients, helping them not just outperform the market, but to Outcreate it. Please also note that neither LTM nor any of its authorized recruitment agencies/partners charge any candidate registration fee or any other fees from talent (candidates) towards appearing for an interview or securing employment/internship. Candidates shall be solely responsible for verifying the credentials of any agency/consultant that claims to be working with LTM for recruitment. Please note that anyone who relies on the representations made by fraudulent employment agencies does so at their own risk, and LTM disclaims any liability in case of loss or damage suffered as a consequence of the same. Recruitment Fraud Alert - https://www.ltm.com/careers/recruitment-fraud-alert

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