Experience: At least 10+ years of hands-on application development experience.
Java Expertise: Strong experience and knowledge in Core Java 11/17 and J2EE.
Microservices: Solid understanding and experience with Microservices architecture (Spring Boot)
and building event driven distributed architecture with microservices.
Expertise on Web Services(REST/SOAP), Design Patterns , Concurrency Framework , Kafka and data structures/Collections, Docker.
Database Knowledge: Basic knowledge of databases such as Oracle/SQL.
Scripting: Basic knowledge of Python.
Problem-Solving: Excellent analytical and problem-solving skills.
Communication: Strong communication and interpersonal skills.
Teamwork: Ability to work effectively in a team environment.
Methodology of AI includes Chunking, Embedding, AI Engineering, Prompt Engineering
Deep hands-on experience in engineering and executing scalable enterprise solutions.
Expert-level proficiency in Python (e.g., FastAPI, Flask, PySpark) or Java (e.g., Spring Boot, Spring Cloud, Spring Security).
Proficiency in UI (e.g., Angular, React, Node.js, TypeScript) for full-stack development.
Proficiency in database technologies, such as Oracle, Postgres, or MongoDB.
Solid understanding of core AI concepts, including knowledge representation, automated planning, decision-making under uncertainty, and multi-agent systems.
Hands-on experience with relevant frameworks (e.g., Google ADK, LanGraph, LangChain, AutoGen, CrewAI, N8N).
Extensive experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and relevant libraries (e.g., Scikit-Learn, NumPy, Pandas).
Proven experience in creating, deploying, and integrating MCPs (Model Context Protocol) into agentic AI systems.
Deep familiarity with large language models (LLMs) such as ChatGPT, Claude, Gemini, and Llama, including their application within agentic systems.
Demonstrated experience in designing and implementing robust APIs for AI services.
Proficient in software development best practices, including version control (Git), CI/CD pipelines, comprehensive testing, and code reviews. Strong understanding of agile methodologies, application resiliency, and security principles applied to complex AI projects. Proven expertise in system design, application development, and ensuring operational stability for AI initiatives.
Deep experience with application and data architecture patterns and designs. Experience leveraging managed services and existing platforms, with a strong emphasis on API-First Design, microservices, and event-driven architectures.
Hands-on experience with Docker and Openshift.
