Domain Architect

HOW YOU WILL CONTRIBUTE AND WHAT YOU WILL LEARN

• Design and architect end-to-end AI/ML system solutions on the Cloud, ensuring scalability, reliability, and maintainability throughout all project phasesfrom discovery and proof of concept to industrialization, deployment, and ongoing maintenance.
• Collaborate with stakeholders to understand business objectives and translate them into AI-driven solutions aligned with enterprise strategy.
• Propose, implement, and select optimal machine learning algorithms by leveraging advanced statistical tools and data science techniques to uncover insights and generate accurate predictions.
• Research, prototype, and implement state-of-the-art AI/ML techniques to solve complex business problems.
• Act as a technical leader and mentor to AI/ML engineers across projects, guiding junior engineers in defining robust methodologies and evaluating the most effective technical solutions.
• Define and enforce engineering best practices, coding standards, and scalable design patterns. Facilitate technical knowledge sharing sessions, code reviews, and design walkthroughs.
• Built scalable APIs for real-time web application enforcement and integrating applications with AI Pipelines. Collaborated with cross-functional teams to integrate AI-powered features.
• Prepare technical documentation, including design specifications, test cases, and user manuals.

Qualifications

• Bachelors or masters degree in computer science, Engineering, or a related field, with overall 8+ years of experience, including extensive work in the design, development, and deployment of AI/ML systems.
• Strong expertise in machine learning algorithms, with advanced proficiency in Python, SQL, and relational/non-relational databases.
• Deep understanding of ML, Natural Language Processing (NLP), Large Language Models (LLMs), and applied GenAI solutions.
• Demonstrated experience in designing scalable AI/ML architectures and integrating intelligent systems into production-ready, cloud-native applications.
• Strong grip on Kubernetes, Docker, and microservices-based architecture for deployment and orchestration of AI solutions with practical experience on agentic AI frameworks such as LangChain, CrewAI, or AutoGen.

• Prior experience developing AI solutions in the telecom domain, with an understanding of domain-specific challenges and data environments.