Java Architect AI ML

Role & responsibilities:

Architecture & Solution Design

• Lead end-to-end architecture of large-scale, distributed Java applications.
• Design scalable, secure, and high-performance microservices architectures.
• Integrate AI/ML models into enterprise systems, ensuring robust data pipelines and model lifecycle management.
• Evaluate and recommend architecture patterns, frameworks, and best practices.

AI/ML & Data Engineering

• Work with data scientists to productionize ML models and optimize model performance.
• Architect data ingestion, transformation, and feature engineering pipelines.
• Implement MLOps frameworks for CI/CD of machine learning models.
• Apply NLP, predictive analytics, and deep learning concepts where required.

Technical Leadership

• Guide development teams on coding standards, design principles, and architectural alignment.
• Conduct technical reviews, performance analysis, and system optimizations.
• Mentor senior developers and collaborate with cross-functional teams.

Cloud & DevOps

• Architect cloud-native solutions on AWS / Azure / GCP.
• Utilize DevOps tools (Docker, Kubernetes, Jenkins, Git, Terraform) for automation and deployments.
• Implement observability, logging, monitoring, and operational excellence practices.

Stakeholder Collaboration

• Work closely with product managers, business stakeholders, and leadership to translate requirements into architecture.
• Prepare technical documents, solution outlines, PoCs, and architectural roadmaps.

Preferred candidate profile:

• 15+ years

  of experience in Java-based enterprise application development.
• Strong expertise in

  Java, Spring Boot, Microservices, REST APIs, JPA/Hibernate

  .
• Hands-on experience with

  AI/ML frameworks

  : TensorFlow, PyTorch, Scikit-learn, ML libraries, or LLM integrations.
• Experience in building

  ML pipelines

  , data processing, and production-grade model deployment.
• Strong knowledge of

  Kafka

  , MQ systems, and distributed event-driven architectures.
• Proven experience with

  cloud platforms (AWS/Azure/GCP)

  .
• Solid understanding of SQL/NoSQL databases.
• Strong knowledge of

  DevOps practices

  , CI/CD, containerization, and Kubernetes.
• Experience in high-level architectural design, UML, patterns, and documentation.

Good-to-Have Skills

• Experience integrating

  GenAI/LLMs

  into enterprise workflows.
• Knowledge of vector databases (Pinecone, FAISS, Milvus).
• Exposure to big data tech (Spark, Hadoop, Databricks).
• Experience in multi-tenant SaaS systems.
• Certifications in cloud or ML/AI.

Education

• Bachelors or Master’s degree in Computer Science, Engineering, or related field.