Java Enterprise Architect

1. Role Summary

The Java Fullstack Architect (with AI Expertise) is responsible for architecting modern, scalable fullstack solutions while integrating AI/ML capabilities across enterprise applications. This role blends deep fullstack engineering knowledge (Java, microservices, cloud, front-end frameworks) with hands-on experience designing and deploying AI-powered features such as intelligent automation, predictive analytics, NLP, and generative AI components. The architect will guide teams to adopt AI-driven patterns while ensuring performance, security, and maintainability.

2. Key Responsibilities

Architectural Leadership

• Own end-to-end architecture across backend, frontend, data, integrations, and AI components.
• Define standards for microservices, APIs, UI architecture, and AI/ML integration patterns.
• Conduct architectural reviews and establish guidelines for high-quality, scalable solutions.

Backend Engineering (Java)

• Architect and build microservices using Java 11+/Spring Boot, Spring Cloud, and cloud-native services.
• Design event-driven systems, asynchronous patterns, and high-performance data pipelines.
• Integrate AI inference services (e.g., REST, gRPC, model-serving APIs).
• Collaborate with data science teams to productionize ML models (MLOps practices).
• Work with cloud AI services (AWS, Azure, GCP) such as:
• AWS Bedrock/SageMaker
• Azure OpenAI / Azure ML
• GCP Vertex AI
• Architect vector databases, embeddings, and retrieval-augmented generation (RAG) pipelines.
• Ensure ethical AI use, security, guardrails, and compliance.

Frontend Engineering

• Design modular, scalable UIs using Angular/React with strong state management.
• Integrate AI-powered UI features (e.g., copilots, predictive fields, intelligent search).
• Ensure seamless front-end consumption of microservices and AI APIs.

Cloud & DevOps

• Architect cloud-native deployments on AWS/Azure/GCP.
• Design CI/CD pipelines supporting microservices and ML models.
• Implement containerization using Docker/Kubernetes, including GPU workloads where required.

Collaboration & Mentoring

• Mentor fullstack and AI engineers in architecture principles and modern engineering patterns.
• Work closely with product, data science, UX, and security teams.
• Translate business needs into practical AI-enabled technical solutions.

3. Required Skills & Experience

Core Technical Skills

Backend (Java):

• Java 11+, Spring Boot, Spring Cloud, REST, JPA/Hibernate

Frontend:

• Angular/React, Node, TypeScript, HTML, CSS

Architecture:

• Microservices, distributed architecture, DDD, event-driven design

Cloud:

• AWS/Azure/GCP cloud-native architecture

Database:

• Relational + NoSQL (MongoDB, Cassandra), vector DBs (Pinecone, Redis Vector, Milvuspreferred)

AI/ML Skill Requirements

• Experience integrating AI APIs (OpenAI, Azure OpenAI, Vertex AI, Bedrock).
• Understanding of ML lifecycle: model training, evaluation, deployment, monitoring.
• Hands-on experience with Python-based ML frameworks (nice to have):
• PyTorch, TensorFlow, Scikit-learn
• Experience with RAG architecture, embeddings, or LLM orchestration frameworks such as:
• LangChain, Semantic Kernel, Haystack
• Ability to design MLOps workflows (CI/CD for models, model versioning, drift detection).
• Practical experience building AI-driven features in enterprise applications.

Soft Skills

• Strong communication and leadership.
• Ability to translate AI concepts into business value.
• Deep problem-solving and solutioning mindset.

4. Preferred Qualifications

• Bachelor’s or Master’s in Computer Science or related field.
• Certifications:
• Cloud Architect (AWS/Azure/GCP)
• Generative AI / ML certifications (preferred)
• Java Architecture certifications
• Experience with data engineering pipelines, ETL, or streaming platforms (Kafka).
• Exposure to privacy, responsible AI, and security frameworks.

5. Key Performance Indicators (KPIs)

• Quality and scalability of AI-integrated architecture.
• Adoption of AI/ML features across products.
• Reduction in latency, cost, and technical debt.
• Successful production deployment of AI/ML workloads.
• Developer enablement through frameworks and best practices.