Enterprise Architect

Role Overview

This role involves comprehensive design and leadership of architectural frameworks spanning backend, frontend, data management, integration, and AI systems. The Enterprise Architect will establish architectural standards and perform rigorous reviews to ensure scalable and maintainable solutions.

Key Responsibilities

• Lead end-to-end architecture across all technological layers including backend, frontend, AI, and integrations.
• Develop standards for microservices, API strategies, UI frameworks, and AI/ML incorporation.
• Architect Java-based microservices using Java 11+, Spring Boot, and Spring Cloud with event-driven and asynchronous patterns.
• Collaborate with data science teams for ML model deployment and operationalization using MLOps practices.
• Design and implement cloud-native deployments on AWS, Azure, and GCP incorporating cloud AI services such as AWS Bedrock, Azure OpenAI, and GCP Vertex AI.
• Manage vector database architecture and retrieval-augmented generation (RAG) pipelines ensuring ethical AI use, security, and regulatory compliance.
• Lead development of modular and scalable frontend systems using Angular and React integrating AI-powered features.
• Oversee containerization and orchestration with Docker and Kubernetes, including GPU execution setups.
• Mentor engineers in modern architectural methods and collaborate closely with product, UX, data science and security teams.
• Translate complex business requirements into AI-enabled and practical technical designs.

Required Skills & Experience

• Backend: Expertise in Java 11+, Spring Boot, Spring Cloud, REST APIs, JPA/Hibernate.
• Frontend: Experience with Angular, React, Node.js, TypeScript, HTML, and CSS.
• Architecture: Strong knowledge of microservices, distributed systems, Domain-Driven Design, and event-driven development.
• Cloud Platforms: Extensive experience with AWS, Azure, and GCP cloud-native architectures.
• Databases: Skilled in both relational and NoSQL databases including MongoDB and Cassandra; experience with vector databases like Pinecone, Redis Vector, or Milvus.
• AI/ML: Proven integration with AI APIs (OpenAI, Azure OpenAI, Vertex AI, Bedrock) and understanding of the ML lifecycle stages.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn is advantageous.
• Experience with retrieval-augmented generation (RAG) architectures and orchestration frameworks (LangChain, Semantic Kernel, Haystack).
• Capability in designing MLOps workflows including CI/CD for models, versioning, and drift detection.

Soft Skills

• Excellent communication and leadership abilities.
• Strong problem-solving skills and the capacity to correlate AI technologies with business outcomes.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science or a related field.
• Relevant certifications such as Cloud Architect (AWS, Azure, GCP) and Generative AI or Java architecture certifications.
• Knowledge of data engineering pipelines, ETL processes, streaming platforms like Kafka.
• Awareness of privacy considerations, responsible AI principles, and security frameworks.

Performance Indicators

• Quality, scalability, and adoption of AI-enabled architectures in products.
• Reduction in latency, cost savings, and lowering technical debt levels.
• Successful deployment and operational stability of AI/ML workloads.
• Developer engagement and enablement via best practices and frameworks.