About This Role

12 to 15 years of experience
Skills: Spring Boot, Spring Cloud, REST, Angular/React, Node, Java 11, Cloud Native, AI, Distributed architecture

The Java Fullstack Architect (with AI Expertise) is responsible for architecting modern, scalable full stack solutions while integrating AI/ML capabilities across enterprise applications. This role blends deep full stack 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.

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 (MLOpspractices).

- 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 solid 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.

Required Skills & Experience

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, Milvus—preferred)

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.

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.

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.

📌 Enterprise Architect (India)
🏢 Bounteous
📍 India