AI Architect

Themesoft Inc.; Hyderabad; Hybrid; INR 2,000,000 - 3,000,000; Full time

Job summary

A leading IT solutions provider in India is seeking an experienced Architect to lead the development of generative AI solutions. Candidates should have at least 10 years of hands-on experience, with deep expertise in Azure/AWS cloud architectures and knowledge in containerization, AI technologies, and observability. This hybrid role requires strong programming skills in Python and the ability to mentor a team, ensuring innovative and ethical AI practices are sustained.

Qualifications

• Minimum 10 years of hands-on experience in software development with focus on AI and cloud architectures.
• Solid programming skills in Python with knowledge in AI/ML frameworks.
• Proficient in implementing and monitoring Large Language Models.

Responsibilities

• Architect and develop innovative generative AI solutions on Azure/AWS.
• Implement and maintain containerised AI applications using Docker.
• Design observability frameworks for LLMs to monitor performance metrics.

Skills

Python programming

AI/ML frameworks

GenAI development

Azure/AWS cloud services

Docker

Kubernetes

CI/CD pipelines

MLOps principles

Tools

Docker

Kubernetes

Langfuse

Datadog

Job description

Location: PAN India (client’s Office Locations)

Work Mode: Hybrid

Notice Period: Immediate to 30 days

Yrs of exp/Level: 10-20 Yrs, SA/M/SM Level

Job Summary

• We are seeking a highly experienced Architect with excellent hands‑on skills to lead GenAI based development and implementation experience for the clients.
• The candidate should have at least 10 years of experience in a technical development role and possess deep expertise in Azure/ AWS cloud‑based architectures, containerization, orchestration, observability and with a focus on AI.
• The role requires a deep understanding of the latest AI technologies and techniques, as well as the ability to apply them to create advanced generative systems.

Key Responsibilities

• Architect and develop innovative generative AI solutions with a focus on scalability and efficiency on the Azure /AWS cloud platform.
• Implement and maintain containerised AI applications using Docker and manage deployments using Kubernetes.
• Design and set up observability frameworks for LLMs to monitor performance metrics, system health, and usage patterns.
• Collaborate with cross‑functional teams to ensure seamless integration of AI capabilities into various products and services.
• Lead performance tuning, troubleshooting, and optimisation of generative AI systems.
• Develop best practices for secure, scalable, and maintainable AI deployments on Azure/AWS.
• Guide and mentor a team of AI engineers in adopting and implementing cutting‑edge technologies and practices.
• Stay updated with the latest advancements in AI, particularly in the areas of generative models and cloud computing.
• Advocate for responsible AI development, ensuring ethical considerations and privacy compliance are embedded in our AI systems.

Qualification

• A minimum of 10 years of hands‑on experience in software development with a strong focus on AI and Azure / AWS cloud architectures.
• Solid programming skills in Python, with expertise in AI/ML frameworks and strong knowledge in Azure OpenAI / Bedrock.
• Excellent knowledge in GenAI development related frameworks to develop and implement Generative AI based solutions.
• In‑depth knowledge of Azure/AWS cloud services and management tools.
• Proficient with Docker and Kubernetes for orchestrating containerised applications.
• Demonstrable experience with implementing and monitoring Large Language Models.
• Experience with implementing CI/CD pipelines and familiarity with DevOps practices.
• Strong analytical and problem‑solving skills, with the ability to lead complex technical initiatives.

Preferred Skills

• Experience with LLM observability tools (e.g., Langfuse, Datadog, etc).
• Knowledge of machine learning model lifecycle management and MLOps principles.
• Knowledge of machine learning model lifecycle management and MLOps principles.