ROLE OVERVIEW

As a Senior Solution Architect – AI & ML, you will be a strategic technology leader responsible for architecting enterprise-scale AI and data solutions that drive innovation, intelligence and transformation. You will own the Data Platform & Advanced Analytics and AI technical support including architectural design sessions, specific implementation projects, build MVPs and trouble solving and prevent the issues and support to build more efficient systems with Well-Architect framework. You will play a critical role in shaping the foundational architecture of a newly established global technology center, influencing both near-term delivery and long-term technology strategy.

This role demands deep technical expertise, architectural vision, and the ability to lead cross-functional teams in building intelligent, data-driven systems that are scalable, secure and future-ready.

JOB REQUIREMENTS

Education and Certifications

·         Bachelor’s or Master’s degree in computer science, artificial intelligence, data engineering or a related technical field

·         Cloud Architecture certification like (Google Cloud Professional Architect, etc.) is preferred

·         Certification in Data Engineering like (Google Cloud Data Engineer, Databricks Certified Data Engineer, etc) is preferred

Required Experience

·         10 – 15 years of experience in software and solution architecture, with at least 7-8 years in leading AI, ML and data-intensive projects

·         Proven leadership in designing and delivering large-scale, cloud-native AI and data platforms

·         Experience in driving enterprise-wide technology strategy, innovation programs and architecture governance

Essential skills

·         Ability to evaluate and integrate Gen AI technologies including LLMs, RAG, and agentic frameworks

·         Expert in designing AI/ML systems using TensorFlow or PyTorch

·         Deep knowledge of cloud-native architecture and AI/ML services on AWS, Azure, or GCP

·         Skilled in building scalable APIs, microservices, and event-driven systems

·         Proficient in MLOps and DataOps using MLflow, Kubeflow, and CI/CD pipelines

·         Strong experience with data engineering tools like Apache Spark, Kafka and Airflow

Desired skills

·         Exposure to multi-agent systems and autonomous AI architectures

·         Familiarity with enterprise data platforms

·         Experience with semantic search, vector databases, and graph technologies

·         Strong understanding of data governance, security, and compliance in distributed systems

ROLES & RESPONSIBILITIES

Strategy and Planning

·         Define the AI and data architecture vision and roadmap for the global technology center, aligned with enterprise digital transformation goals

·         Drive technology strategy and innovation planning, identifying opportunities for AI-driven differentiation and operational efficiency

·         Influence enterprise architecture decisions, ensuring AI and data capabilities are embedded into core business platforms

·         Evaluate build vs. buy decisions, vendor solutions, and open-source technologies to accelerate innovation

Delivery and Execution

·         Architect and lead the development of AI and data platforms that support enterprise-scale applications, including Gen AI, predictive analytics, and intelligent automation

·         Design end-to-end solutions integrating LLMs, RAG pipelines, real-time data processing, and cloud-native services

·         Define and enforce architectural standards, patterns, and best practices across engineering teams to ensure consistency, scalability, and maintainability

·         Lead technical evaluations and PoCs for emerging AI and Gen AI technologies, ensuring alignment with business goals and technical feasibility

·         Collaborate with engineering, product, and data science teams to translate complex business requirements into robust, scalable architectures

Support and Enablement

·         Contribute to reusable architecture assets, reference implementation and internal accelerators

·         Engage with external partners, vendors, and industry forums to stay ahead of AI and data trends

·         Champion responsible AI practices, ensuring fairness, transparency and compliance in all AI solutions

People Leadership

·         Mentor and guide solution architects, data engineers, and AI specialists, fostering a high-performance, innovation-driven culture

·         Support talent development and succession planning, helping build a strong bench of future technical leaders