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Job Overview:

The primary purpose of this role is to establish a robust architectural foundation for AI/ML and data platforms, aligning with cloud-native principles to ensure high-performance density and cost-efficiency. You are responsible for the entire lifecycle of AI initiatives, ensuring they deliver tangible business outcomes and measurable ROI. This involves translating high-level business requirements into detailed Solution Intents and High-Level Designs (HLDs) while maintaining strict technical and architectural standards across the organization. As the Senior Manager for AI Solution Design and Architecture, you will lead the enterprise-wide strategy for AI-driven solutions, data platforms, and analytics capabilities. This leadership role focuses on architecting innovative and scalable AI solutions that address complex business challenges while ensuring seamless integration into existing business operations. You will drive the organizational AI roadmap, leveraging emerging trends in Generative AI, Edge Computing, and IoT to foster a competitive advantage. By collaborating with cross-functional stakeholders, including Data Scientists and Engineers, you will ensure all designs are strategically impactful and optimize long-term growth.

Key Responsibilities:

• Lead the design and implementation of robust, scalable, and innovative AI solutions, overseeing the entire end-to-end AI solution blueprint and roadmap ensuring alignment with the ATOM’s technical and architectural standards
• Work closely with business leaders and key stakeholders to translate AI architecture requirements into strategic architecture solutions
•  Collaborate with teams to ensure seamless integration of AI models and platforms into existing infrastructure, such as CRM systems, Campaign, APIs, databases, and cloud platforms (AWS, GCP, Azure) and other critical business applications
• Define and govern reusable patterns, including ingestion, data modelling, integration, and AI/ML deployment frameworks, ensuring consistency and quality across domains
• Responsible for the maintenance of comprehensive documentation of AI solution designs, architectures, model configurations, and deployment procedures, ensuring that design decisions, assumptions, and constraints are clearly communicated and easily accessible for future reference 
• Oversee vendor and tooling evaluation, ensuring technology choices align with enterprise needs, resilience, and long-term roadmap. 
• Ensure AI solutions adhere to stringent security and privacy requirements, implement encryption, authentication, and authorization protocols to safeguard data across ATOM’s AI systems.
• Drive innovation in AI, automation, and cloud architecture, introducing modern solutions that accelerate business outcomes and reduce technical debt
• Leverage feedback from business users, technical teams, and external sources to refine AI solutions, ensuring that the solutions continue to evolve and meet changing business needs

​​​​​Education:

• Bachelor’s degree in computer science, Information Technology, Engineering Bachelor of Engineering
• Master’s in computer science

Experience

• Overall experience 10+ years

• 5+ years of experience in AI solution design and architecture, with a proven track record of delivering AI-driven solutions in large-scale, complex telecom environments

• Strong experience with AI technologies, including machine learning, deep learning, and data engineering

• Hands-on experience with integrating AI solutions into existing business systems, such as CRM, CMP, and other enterprise platforms

• Strong knowledge of MLOps, data governance, streaming, analytics platforms, FinOps, and cloud-native patterns (AWS/GCP/Azure). 

• Good to have a relevant certification in AI/ML, Cloud Computing, AWS Certified ML, Microsoft Certified: Azure AI Engineer
   

Skillsets

• Proficiency in AI and machine learning frameworks

• Extensive experience in enterprise data architecture, AI/ML architecture, and cloud-native platform design

• Strong understanding of data engineering principles and tools for handling large-scale data pipelines (e.g., Hadoop, Spark, Kafka)

• Experience with MLOps practices for managing AI model lifecycles