JAVA + AI Architect

Job Summary

We are seeking a highly experienced Java + AI Architect to lead the design and implementation of enterprise-scale applications powered by Artificial Intelligence and Generative AI. The ideal candidate will have deep expertise in Java-based application architecture, cloud-native technologies, AI/ML platforms, Large Language Models (LLMs), and enterprise integration patterns.

Key Responsibilities

• Define and drive the architecture for Java-based enterprise applications integrated with AI capabilities.
• Design scalable, secure, and high-performance AI-powered solutions using modern architectural principles.
• Lead the adoption of Generative AI, Machine Learning, and Agentic AI across enterprise platforms.
• Architect and implement AI use cases such as chatbots, intelligent assistants, document processing, knowledge search, and recommendation engines.
• Design Retrieval-Augmented Generation (RAG) frameworks and vector-based search architectures.
• Integrate LLMs and AI services with enterprise systems using APIs and microservices.
• Collaborate with business stakeholders, product teams, engineering teams, and leadership to translate business requirements into technical solutions.
• Define best practices, coding standards, security standards, and architecture guidelines.
• Provide technical leadership, mentoring, and guidance to development teams.
• Drive solution reviews, performance optimization, and scalability assessments.

Required Skills

Java Architecture

• Strong expertise in Java 8/11/17+.
• Extensive experience with:
  • Spring Boot
  • Spring Cloud
  • Microservices Architecture
  • REST APIs
  • Event-Driven Architecture
  • Distributed Systems
• Experience with:
  • Kafka
  • RabbitMQ
  • ActiveMQ
• Strong understanding of:
  • Design Patterns
  • Domain-Driven Design (DDD)
  • SOLID Principles
  • Enterprise Integration Patterns

AI & Generative AI

• Deep understanding of:
  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Generative AI
  • Large Language Models (LLMs)
• Experience with:
  • OpenAI
  • Azure OpenAI
  • AWS Bedrock
  • Google Vertex AI
  • Anthropic Claude
  • Open-Source Models (Llama, Mistral, etc.)
• Hands-on experience in:
  • Prompt Engineering
  • Fine-Tuning
  • RAG Architecture
  • Vector Databases
  • AI Agents
  • AI Governance

AI Frameworks & Tools

• Experience with:
  • LangChain
  • LangGraph
  • LlamaIndex
  • Semantic Kernel
  • CrewAI
  • AutoGen
• Vector Databases:
  • Pinecone
  • ChromaDB
  • Weaviate
  • FAISS

Cloud & DevOps

• Expertise in one or more cloud platforms:
  • Microsoft Azure
  • AWS
  • Google Cloud Platform (GCP)
• Experience with:
  • Docker
  • Kubernetes
  • OpenShift
  • CI/CD Pipelines
  • Terraform

Data Technologies

• Strong knowledge of:
  • SQL
  • NoSQL Databases
  • Data Warehousing
  • Data Pipelines
  • Apache Spark
  • Databricks

Leadership Responsibilities

• Lead architecture governance and technology roadmap initiatives.
• Conduct architecture and code reviews.
• Mentor architects, technical leads, and development teams.
• Evaluate new AI technologies and recommend adoption strategies.
• Engage with clients and leadership teams on solution strategy and innovation initiatives.

Preferred Skills

• Experience in building enterprise AI platforms.
• Knowledge of MLOps and Model Lifecycle Management.
• Experience with AI security, Responsible AI, and compliance frameworks.
• Exposure to Retail, BFSI, Healthcare, or Manufacturing domains.
• AI and Cloud certifications from Microsoft, AWS, Google, or equivalent.

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field.

Soft Skills

• Excellent stakeholder management and communication skills.
• Strong analytical and problem-solving abilities.
• Strategic thinking and decision-making capabilities.
• Ability to lead globally distributed teams.
• Strong consulting and client-facing skills.

Nice to Have

• Experience designing enterprise-scale AI transformation programs.
• Knowledge of Multi-Agent Architectures and Autonomous AI Systems.
• Published technical papers, patents, or conference presentations in AI or enterprise architecture.
• Experience with AI Centers of Excellence (CoE) and innovation initiatives.