Full Stack AI Application Engineer

FULL STACK AI APPLICATION ENGINEER

**Python / Java Generative AI, RAG & Agentic AI** **Experience: 3 to 7 years   Location: India / Remote / Offshore   Employment: Full-Time / Contract**

About NForce One

NForce One is a technology services and solutions company with delivery capability across the United States and India. We build AI-powered products, enterprise applications, accelerators, and client-specific solutions across Generative AI, Agentic AI, Quality Engineering, Data Engineering, Cloud, and Digital Transformation.

Role Overview

We are looking for a hands-on Full Stack AI Application Engineer who is a strong software engineer first and an AI practitioner second. You will build complete, production-ready applications end to end, combining solid Python and Java engineering with modern front-end development and Generative AI, RAG, and Agentic AI capabilities.

You will own work across the full lifecycle: business requirements, solution design, backend development, database and vector store, LLM and agent integration, APIs, user interface, testing, cloud deployment, and production support. This is not a research, notebook-only, or model-experimentation role.

Key Responsibilities

·       Design and develop AI-powered enterprise applications, assistants, copilots, intelligent search, and workflow automation solutions.

·       Integrate commercial and open-source LLMs into production systems using prompt engineering, context engineering, structured outputs, tool and function calling.

·       Build end-to-end RAG pipelines covering document ingestion, chunking, embeddings, vector storage, hybrid and semantic retrieval, metadata filtering, reranking, context construction, evaluation, and hallucination reduction.

·       Build Agentic AI workflows including multi-agent systems, agent memory, planning, orchestration, supervisor agents, and human-in-the-loop flows.

·       Develop backend services, REST APIs, and microservices in Python (FastAPI, Flask, Django) and Java (Spring Boot), with proper authentication, validation, error handling, logging, and versioning.

·       Build responsive front-end interfaces with React or Angular and wire them to backend and AI services, including streaming LLM responses, chat interfaces, and authentication flows.

·       Design and work with relational, NoSQL, and vector databases.

·       Deploy, monitor, and support applications on Azure, AWS, or GCP using Docker, CI/CD pipelines, and standard observability tooling.

·       Build reusable AI services and components, and evaluate emerging AI frameworks and models for adoption.

·       Contribute to AI-powered automation and quality engineering initiatives such as test generation, defect triage, root cause analysis, and self-healing automation.

·       Translate business requirements into technical solutions, build rapid POCs, and convert successful POCs into production applications.

Required Qualifications

·       3+ years of professional software development experience.

·       Strong hands-on Python development, writing maintainable production code (not limited to notebooks).

·       Hands-on Java and Spring Boot development experience.

·       Proven experience designing and building REST APIs and backend services.

·       Full stack application development experience, including hands-on front-end work with JavaScript or TypeScript and React or Angular, plus HTML and CSS.

·       Strong SQL and relational database experience (PostgreSQL, MySQL, SQL Server, or Oracle).

·       Hands-on Generative AI / LLM integration experience in real applications.

·       Experience building or contributing to RAG-based applications.

·       Hands-on experience with at least one major cloud platform (Azure, AWS, or GCP).

·       Proficiency with Git and strong software engineering fundamentals.

Preferred Qualifications

·       4 to 7 years of overall IT experience, including 2+ years each in Python and Java application development.

·       Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or Semantic Kernel.

·       Experience building and deploying AI agents in production.

·       Vector database experience: Pinecone, Weaviate, ChromaDB, FAISS, pgvector, Azure AI Search, or OpenSearch.

·       Microservices architecture, Docker, Kubernetes, and CI/CD (Jenkins or GitHub Actions).

·       Practical ML understanding (classification, regression, clustering, fe…