Enterprise Architect

Bounteous; Gurugram District, Bengaluru; Hybrid; INR 4,000,000 - 6,500,000; Full time

Job summary

Bounteous is seeking an Architect/Lead for India (Gurugram) to own end-to-end architecture across backend, frontend, data, integrations, and AI components. You will define standards for microservices, APIs, and AI/ML integration patterns, and guide architectural reviews to ensure scalable solutions.

The role requires Java 11+/Spring Boot, cloud-native deployment, and cross-functional collaboration with data science, UX, and security teams.

Qualifications

• Lead 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 scalable solutions.
• Architect and build microservices with Java 11+/Spring Boot and cloud-native services.
• Design event-driven systems, asynchronous patterns and high-performance data pipelines.

Responsibilities

• Own end-to-end architecture across backend, frontend, data, integrations, and AI components.
• Architect microservices and AI integration patterns to scale across platforms.
• Mentor engineers in architecture principles and modern engineering practices.

Skills

Technical leadership

Architectural design

Cloud architecture

AI integration

Frontend integration

Mentoring

Education

Bachelor’s or Master’s in Computer Science

Tools

Java 11+/Spring Boot

Spring Cloud

Angular/React

Docker/Kubernetes

Pinecone/Milvus/Vector DBs

Job description

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, 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.

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.