Job Title: Java Full Stack Developer - Advanced
Location: New York
Duration: Long Term
Job Overview: 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.
Key Skills / Search Keywords: 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
Key Responsibilities
- 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.
Required Technical Skills Backend
- Strong hands-on experience with Java 8/11/17+
- Spring Boot, Spring MVC, Spring Security
- Microservices architecture
- RESTful APIs and API integration
- Hibernate / JPA
- SQL and relational databases
- Experience with messaging systems such as Kafka/RabbitMQ is preferred
Frontend
- Strong experience with React.js or Angular
- JavaScript / TypeScript
- HTML5, CSS3
- Responsive UI development
- Frontend-backend API integration
Agentic AI / GenAI
- Understanding of Generative AI, LLMs, and Agentic AI
- Hands-on experience integrating LLMs into applications
- Experience with one or more agentic frameworks such as:
- LangChain
- LangGraph
- CrewAI
- AutoGen
- Semantic Kernel
- Google ADK / AWS Bedrock Agents
- Knowledge of RAG, embeddings, vector databases, prompt engineering, tool/function calling, and agent orchestration
- Exposure to MCP / Model Context Protocol is an advantage
- Understanding of LLM evaluation, observability, guardrails, and responsible AI is preferred
Cloud & DevOps
- Experience with at least one cloud platform: AWS / Azure / Google Cloud Platform
- Docker and Kubernetes
- CI/CD pipelines
- Git / GitHub / GitLab / Bitbucket
- Cloud-native application development
- Experience with cloud-based AI services is preferred
Data & Integration
- Strong SQL and database fundamentals
- Experience with relational and NoSQL databases
- Exposure to vector databases such as Pinecone, Weaviate, Milvus, OpenSearch, or similar
- Experience integrating enterprise APIs, third-party services, and data platforms
- Understanding of data pipelines and event-driven architectures is a plus
Forward Deployed Engineer Capabilities
- Ability to work directly with client engineering and business teams.
- Strong problem-solving skills and ability to operate in ambiguous environments.
- Ability to rapidly prototype and demonstrate working solutions.
- Strong communication and stakeholder-management skills.
- Ability to explain technical concepts and architecture decisions to both technical and non-technical audiences.
- Comfortable owning solutions end-to-end—from discovery and design through development, deployment, and production support.
- Willingness to work closely with customers and adapt solutions based on real-world feedback.
Experience
- 5+ years of experience in software/full-stack development.
- Strong commercial experience in Java Full Stack Development.
- At least 1–2 years of hands-on exposure to GenAI / Agentic AI / AI-enabled application development preferred.
- Experience delivering enterprise applications in production environments.
- Experience working with large enterprise clients is a strong advantage.
- BFSI / Healthcare / Retail / Telecom or other regulated-industry experience is preferred.
Preferred Qualifications
- 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.
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