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We are seeking an experienced AI Application Engineer with hands-on experience to develope scalable AI applications and integrating them with enterprise systems for our healthcare client in the US.

This role focuses on building production-ready AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and enterprise integrations.

Key Responsibilities

- Design, develop, and deploy enterprise AI applications using Large Language Models (LLMs).
- Build intelligent AI assistants and automation solutions for engineering and operational workflows.
- Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources and internal documentation.
- Design and implement AI agents capable of performing multi-step workflows using APIs and external tools.
- Integrate AI applications with enterprise platforms, cloud services, databases, and REST APIs.
- Develop secure, scalable, production-ready AI solutions following enterprise security and governance standards.
- Evaluate and improve AI application quality through prompt engineering, retrieval optimization, model evaluation, and guardrails.
- Collaborate with cross-functional teams to identify and implement recent AI use cases across cloud engineering, platform engineering, and security.

Required Qualifications

Software Engineering





- Bachelor's degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.
- 5+ years of software engineering experience.
- Strong proficiency in Python.
- Experience building REST APIs using FastAPI or similar frameworks.
- Strong understanding of software design patterns and distributed systems.
- Experience with Git, automated testing, and CI/CD pipelines.
- Experience developing containerized applications using Docker.
- Familiarity with Kubernetes and cloud-native application development.

Generative AI (hands-on experience required)

- Large Language Models — OpenAI, Azure OpenAI, Gemini, Claude, or similar.
- Prompt engineering.
- Retrieval-Augmented Generation (RAG).
- Embeddings and semantic search.
- Vector databases.
- AI agents / agentic AI.
- Function calling / tool calling.
- AI application evaluation and optimization.
- AI safety, guardrails, and hallucination reduction.

Cloud & Enterprise Technologies

- Experience with one or more public cloud platforms: Microsoft Azure, Google Cloud Platform (GCP), or Amazon Web Services (AWS).
- Working knowledge of Identity and Access Management (IAM), cloud security best practices, REST APIs, enterprise authentication and authorization, infrastructure automation, and cloud governance.

📌 AI Application Engineer (India)
🏢 Xsell Resources
📍 India