Position Title: Java Fullstack Architect

Location:
Bangalore, Hyderabad, Employment Type: Full‑time Mode of work
- Hybrid. 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 up-to-date 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, 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. 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.

📌 Java Fullstack Enterprise Architect (Bengaluru)
🏢 Bounteous
📍 Bengaluru