Enterprise Architect

Bounteous -
Bengaluru, Karnataka

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Job details

Full-time
5 days ago

Qualifications

Spring Boot
TensorFlow
CI/CD
Azure
Cloud architecture
Node.js
Law
Computer Science
CSS
Cassandra
React
Kubernetes
PyTorch
Full-stack development
Software deployment
Enterprise Software
NoSQL
MongoDB
Java
Master's degree
Microservices
OS Kernels
AWS
Docker
Bachelor's degree
Continuous integration
Redis
JPA
REST
Natural language processing
Angular
APIs
ETL
Kafka
Data science
AI
gRPC
TypeScript
Communication skills
Python
Spring framework
HTML
Hibernate

Full job description

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 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.

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.

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