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# Full Stack Java Technical Specialist - React.js

- **Company:** HCL America Inc.
- **Location:** Durham, NC, United States
- **Salary:** $189,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Application Performance Management, Microsoft Azure, COBOL (Programming Language), Data Integration, Extract Transform Load (ETL), DevOps, Distributed Systems, Graph Database, Python (Programming Language), Neo4j, Node.Js, NoSQL, Rapid Prototyping Process, Cloud Services, Software Engineering, SQL Databases, TypeScript, Management of Software Versions, Web Application Frameworks, Data Logging, Cloud Platform System, GitHub Copilot, ReactJS, Large Language Models, Grafana, Prompt Engineering, Apache Spark, Generative AI, Backend, AngularJS, Kubernetes, Machine Learning Operations, Front End Software Development, Restful APIs, Terraform, Splunk, Data Pipelines, Serverless Computing, Docker
- **Published:** June 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=86a514b2ddb169b4

## About the Role

Do you have experience in TypeScript?, Programming & Software Engineering (Required)

Strong proficiency in Python (primary language)
Experience in JavaScript / TypeScript
Backend frameworks: Node.js / Angular
Exposure to Java or .NET ecosystems
Strong experience in SQL / NoSQL databases
Experience with Graph Databases (Neo4j, Cypher)

Full-Stack & Integration
API development and consumption (REST services)
Frontend development (Angular / TypeScript)
Data integration: APIs, workflows, and data orchestration
Experience with data pipelines (ETL/ELT)

Cloud & DevOps (Hands-on Delivery Focus)
Experience with at least one cloud: AWS / Azure / GCP
Hands-on usage of core cloud services (compute, serverless, NoSQL, etc.)

AI / ML Specialization (Critical Requirement)
Strong understanding of LLM fundamentals (transformers, prompting)
Hands-on experience building RAG pipelines end-to-end

Other Requirements

Good to Have

Containers (Docker) and orchestration (Kubernetes)
Infrastructure as Code (Terraform)
Observability tools (Splunk, OpenTelemetry, tracing)
Vector databases (Pinecone, Weaviate, PGVector)
Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI
AI evaluation, guardrails, and observability
MLOps fundamentals (deployment, monitoring, versioning)
Responsible AI practices

Experience with Prompt Engineering
Familiarity with AI coding assistants (GitHub Copilot, Codex)
Exposure to mainframe / COBOL environments
Participation in GenAI hackathons or rapid prototyping initiatives
Understanding of basic distributed systems (Spark or equivalents)

## Description

We are seeking a Forward Deployment AI Engineer to join our innovation team focused on leveraging modern technologies, including Generative AI, to accelerate software development and deliver impactful solutions.
In this role, you will work across the full stack, build prototypes and production-ready applications, and contribute to the design and implementation of AI-driven capabilities, including LLM-based solutions.
You will collaborate with cross-functional teams to evaluate emerging technologies and translate ideas into scalable solutions that improve engineering productivity and business outcomes., Design, develop, and deploy end-to-end AI-powered applications across frontend, backend, and AI layers
Build and integrate LLM-based solutions, including RAG pipelines and prompt engineering workflows
Develop and consume REST APIs and implement seamless system integrations
Build full-stack applications using modern frameworks (Python, Node.js, Angular/TypeScript)
Design and manage data pipelines (ETL/ELT) and work with relational, NoSQL, and graph databases
Deploy and manage applications in cloud environments (AWS/Azure/GCP) using core cloud services
Ensure scalability, performance, and reliability of applications through effective system design
Implement monitoring, logging, and evaluation mechanisms for AI and application performance
Collaborate with business and technical stakeholders to translate requirements into technical solutions
Contribute to rapid prototyping, innovation initiatives, and continuous improvement of engineering practices


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