Backend Developer with Java Spring Boot Python FastAPI Cloud GenAI

Job Description: Senior Backend Developer Java Spring Boot / Python FastAPI / Cloud / GenAI

Location: [Bangalore, India]
Employment Type: Full-time

About the Role

We are looking for an experienced Senior Backend Developer with strong expertise in Java Spring Boot, Python FastAPI, SQL/NoSQL databases, cloud platforms, and modern deployment practices.

The ideal candidate should also have exposure to Generative AI, Agentic AI workflows, AI agents, LLM-based applications, and integration of AI services into backend systems. This role requires a strong backend engineering mindset with the ability to design scalable APIs, microservices, cloud-native applications, and AI-powered backend solutions.

Key Responsibilities

• Design, develop, and maintain scalable backend services and APIs using Java Spring Boot, Python FastAPI.
• Build and manage RESTful APIs, microservices, and event-driven backend systems.
• Work with both SQL databases such as PostgreSQL, MySQL, SQL Server, Oracle, etc., and NoSQL databases such as MongoDB, DynamoDB, Cassandra, Redis, etc.
• Develop secure, high-performance, and reliable backend systems.
• Build and integrate Generative AI-powered features into backend applications.
• Design and implement AI agent workflows, agentic AI solutions, and LLM-based applications.
• Integrate backend systems with LLMs and AI platforms such as OpenAI, Azure OpenAI, Google Vertex AI, AWS Bedrock, Anthropic Claude, or similar platforms.
• Work on prompt engineering, RAG-based solutions, vector databases, embeddings, and semantic search.
• Build APIs and orchestration layers for AI agents, tools, function calling, and automated workflows.
• Implement AI model integrations with enterprise systems, databases, third-party APIs, and cloud services.
• Work on cloud-based application development and deployment using at least two platforms among AWS, Azure, and GCP.
• Experience with event-driven architecture and messaging systems like Kafka, RabbitMQ, AWS SQS/SNS, Azure Service Bus, or Google Pub/Sub.
• Manage application deployments using CI/CD pipelines, Docker, Kubernetes, and DevOps tools.
• Optimize application performance, database queries, system scalability, and AI service usage costs.
• Implement authentication, authorization, logging, monitoring, observability, and error-handling mechanisms.
• Collaborate with frontend developers, QA engineers, DevOps teams, product managers, and AI/ML teams.
• Participate in code reviews, architecture discussions, technical design, and documentation.
• Troubleshoot production issues and provide timely resolutions.
• Knowledge of API gateways, service mesh, and distributed system design.
• Experience with unit testing and integration testing frameworks.

Required Skills and Experience

• 10+ years of experience in backend development.
• Strong hands-on experience with Java and Spring Boot.
• Good experience with Python and FastAPI.
• Strong knowledge of Node.js and backend API development.
• Experience in designing and developing REST APIs, microservices, and scalable backend architectures.
• Strong knowledge of SQL databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Good experience with NoSQL databases such as MongoDB, DynamoDB, Cassandra, or Redis.
• Hands-on experience with at least two cloud platforms among AWS, Azure, and GCP.
• Experience in application deployment, cloud services, and infrastructure management.
• Good knowledge of Docker, Kubernetes, CI/CD pipelines, and deployment automation.
• Experience with version control tools such as Git, GitHub, GitLab, or Bitbucket.
• Understanding of application security, authentication, and authorization mechanisms such as OAuth2, JWT, SSO, etc.
• Exposure to Generative AI, LLMs, AI agents, or Agentic AI frameworks.
• Experience integrating AI/LLM APIs such as OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic, or similar.
• Understanding of prompt engineering, embeddings, vector databases, RAG, and AI workflow orchestration.
• Strong debugging, problem-solving, and performance optimization skills.
• Good understanding of Agile/Scrum development methodology.

AI / GenAI / Agentic AI Skills

The candidate should have experience or strong understanding of:

• Generative AI application development using LLMs.
• Building AI agents and autonomous workflows.
• Working with Agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
• Implementing tool calling / function calling for AI agents.
• Designing multi-agent workflows and AI orchestration patterns.
• Building RAG pipelines using embeddings, vector databases, and document retrieval.
• Working with vector databases such as Pinecone, Weaviate, Milvus, FAISS, ChromaDB, Azure AI Search, or OpenSearch.
• Integrating AI solutions with enterprise data sources, APIs, and cloud services.
• Implementing guardrails, validation, logging, monitoring, and security for AI-based systems.
• Understanding responsible AI practices, data privacy, and hallucination mitigation techniques.

Cloud and Deployment Skills

The candidate should have experience in:

• Deploying applications on AWS, Azure, or GCP.
• Working with cloud services related to compute, storage, networking, databases, monitoring, logging, and security.
• Deploying AI-enabled applications and backend services in cloud environments.
• Containerization using Docker.
• Orchestration using Kubernetes or cloud-native container services such as EKS, AKS, GKE.
• CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, AWS CodePipeline, or Cloud Build.
• Monitoring and observability tools such as CloudWatch, Azure Monitor, Google Cloud Monitoring, Prometheus, Grafana, ELK/EFK, etc.

Good to Have

• Knowledge of serverless technologies such as AWS Lambda, Azure Functions, or Google Cloud Functions.
• Experience with Terraform, Ansible, Pulumi, or other Infrastructure as Code tools.
• Knowledge of MLOps, LLMOps, model deployment, prompt versioning, and AI monitoring.
• Experience in building chatbots, copilots, workflow automation agents, or AI-powered enterprise assistants.

Educational Qualification

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, AI/ML, Data Science, or a related field.