We are looking for a Java Full Stack Developer / Forward Deployed Engineer with strong hands-on experience in Java, Spring Boot, modern frontend technologies, APIs, cloud platforms, and enterprise application development.
The ideal candidate will work closely with business and technical stakeholders to design, build, integrate, and deploy AI-powered and Agentic AI solutions for enterprise use cases.
This role requires a strong engineering mindset with the ability to work across the frontend, backend, APIs, data, cloud, and AI/agentic layers, rapidly convert business requirements into working solutions, and support deployments in complex enterprise environments.
Design and develop scalable full-stack applications using Java, Spring Boot, REST APIs, and modern frontend frameworks.
Build responsive user interfaces using React.js / Angular and integrate them with backend services and APIs.
Design and implement microservices-based applications and enterprise integration solutions.
Develop and integrate GenAI and Agentic AI capabilities into enterprise applications and workflows.
Work with AI/agent frameworks such as LangChain, LangGraph, Microsoft Semantic Kernel, CrewAI, AutoGen, or similar frameworks.
Integrate applications with LLMs, RAG pipelines, vector databases, AI APIs, and enterprise data sources.
Develop APIs and tool integrations that enable AI agents to interact with enterprise systems.
Work with MCP (Model Context Protocol) or similar approaches for connecting AI agents with enterprise tools and services.
Build proof-of-concepts rapidly and evolve them into production-ready solutions.
Work directly with client/business stakeholders to understand ambiguous business problems and translate them into technical solutions.
Design and implement workflows involving AI agents, orchestration, tool calling, memory, human-in-the-loop processes, and automated decisioning.
Integrate AI solutions with existing enterprise applications, databases, APIs, and legacy systems.
Implement authentication, authorization, security, logging, monitoring, and governance requirements.
Deploy applications and AI workloads on cloud platforms such as AWS, Azure, or Google Cloud Platform.
Containerize and deploy applications using Docker and Kubernetes.
Implement CI/CD pipelines and follow modern DevOps and software engineering practices.
Perform testing, debugging, performance optimization, and production support.
Collaborate with architects, product managers, AI engineers, data engineers, and client stakeholders.
Document technical designs, reusable components, integration patterns, and deployment processes.
Experience building AI-native applications or agentic workflows.
Experience with LLM APIs such as OpenAI, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Experience with RAG and enterprise knowledge systems.
Experience with AI observability, evaluation, guardrails, and governance.
Exposure to enterprise security concepts such as OAuth2, JWT, IAM, RBAC, and API security.
Experience working in Agile/Scrum environments.
Strong analytical, communication, and client-facing skills.
Java | Spring Boot | Microservices | React.js | Angular | JavaScript | TypeScript | REST API | SQL | AWS/Azure/Google Cloud Platform | Docker | Kubernetes | CI/CD | GenAI | Agentic AI | LLM | RAG | LangChain | LangGraph | CrewAI | AutoGen | Semantic Kernel | MCP | Vector Database | Prompt Engineering | AI Agents | API Integration | Full Stack Development | Forward Deployed Engineer
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