Enterprise Architect:L-A-BHJDJ

Bounteous; Gurugram District, Bengaluru; Hybrid; INR 3,500,000 - 6,000,000; Full time

Job summary

Bounteous in Gurugram, India seeks a Senior Architect to own end-to-end architecture across backend, frontend, data, integrations, and AI components. You will define standards for microservices, APIs, UI architecture, and AI/ML integration patterns.

Lead backend with Java 11+/Spring Boot, design event-driven systems, and integrate AI inference services. Collaborate with data science teams, cloud providers, and security to deliver scalable AI-enabled solutions.

Qualifications

• Experience leading multi-disciplinary teams in enterprise AI projects.
• Strong knowledge of microservices, APIs, and distributed systems.
• Hands-on with cloud-native architectures and ML pipelines.
• Experience integrating AI APIs and ML models into apps.

Responsibilities

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

Skills

Java 11+

Spring Boot

Spring Cloud

REST API design

JPA / Hibernate

Angular / React

Node.js

TypeScript

AI/ML integration

MLOps concepts

Cloud (AWS/Azure/GCP)

Docker

Kubernetes

Vector databases

Education

Bachelor's or Master's in Computer Science or related field

Tools

Docker

Kubernetes

AWS

Azure

GCP

Pinecone

Redis Vector

Milvus

OpenAI API

LangChain

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

• Bachelors 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.