Java Backend with Python and Gen AI

Virtusa Hiring Java Backend developer with combination of Python and Gen AI resources.

we are not looking python developer.

We are looking for a resource who can join immediately (in 1-3 days) after client selection.

If interested pls share updated cv and details via mail for fast approach ([email removed]).

Total Exp:

Java backend exp:

Python exp:

Gen AI Exp:

current ctc:

expected ctc:

official notice:

last working day:

How many days it takes to join after client selection:

Job Description

Enterprise Java & Architecture: The engineer possesses deep expertise in Core Java, Spring Boot, Spring Framework, and Microservices architecture, grounded in SOLID principles and Object-Oriented Programming. They demonstrate advanced command over Java Collections, multithreading, concurrency, algorithms, and design patterns. Their architectural scope includes implementing enterprise integration patterns, API gateways, proxies, caching mechanisms, and distributed messaging platforms such as Kafka and RabbitMQ. They are highly skilled in developing secure RESTful APIs utilizing JWT, OAuth2, and Single Sign-On (SSO), while continuously ensuring application quality through rigorous unit testing, integration testing, secure coding standards, performance tuning, and distributed debugging.

Python & AI/ML Integration: As a strong Python developer utilizing FastAPI and Flask, the candidate specializes in seamlessly integrating Java enterprise applications with large language models (LLMs) across platforms like OpenAI GPT, Azure OpenAI, Google Vertex AI, Anthropic Claude, and AWS Bedrock. They exhibit advanced practical knowledge of Retrieval-Augmented Generation (RAG), vector databases (Pinecone, FAISS, ChromaDB, Milvus), embeddings, semantic search, prompt engineering, AI agents, and function calling. Leveraging orchestration frameworks such as LangChain, LlamaIndex, and Semantic Kerneland familiar with libraries like scikit-learn, TensorFlow, and PyTorchthey design and deploy intelligent assistants, chatbots, document processing pipelines, and workflow automation while ensuring strict adherence to data privacy, governance, and security compliance.

Cloud, DevOps & Data Engineering: The role demands a comprehensive understanding of continuous integration and continuous delivery (CI/CD) pipelines using Jenkins, GitHub Actions, Azure DevOps, or GitLab CI, paired with hands-on containerization and orchestration using Docker, Kubernetes, and OpenShift across major cloud platforms (AWS, Azure, Google Cloud). The engineer maintains continuous visibility into application stability and performance using monitoring suites such as Splunk, ELK Stack, Prometheus, Grafana, and AppDynamics. Their data engineering expertise encompasses schema design, query optimization, and performance tuning across a diverse ecosystem of relational databases (Oracle, MySQL, PostgreSQL) and NoSQL stores (MongoDB, Couchbase, Redis, Cassandra, and Graph databases).