Role Overview
As an AI Application Engineer, you’ll be responsible for integrating, fine-tuning, and operationalizing large language models and other AI components into production-grade software systems. This role focuses on post-training workflows—such as fine-tuning, prompt engineering, retrieval-augmented generation (RAG), vector database integration, and model deployment—rather than foundational model development.
You’ll work closely with backend engineers, product teams, and data teams to build intelligent apps that are?fast, accurate, consistent and production-ready.
Key Responsibilities
- Integrate LLMs and other AI components into web and backend applications.
- Fine-tune open models using techniques like LoRA, QLoRA, or supervised fine-tuning.
- Build RAG pipelines using vector databases.
- Design and optimize prompts for various tasks (prompt chaining, templating, few-shot setups).

- Package and deploy models for inference?
- Evaluate and monitor AI model performance in live environments.
- Ensure scalable, secure, and compliant use of AI systems in production.
- Stay informed on LLM tooling ecosystems (LangChain, LlamaIndex, OpenAI APIs, Hugging Face, Spring AI etc.)
Required Qualifications

- Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
- 2+ years of experience building AI-augmented applications or services.
- Proficient in Python and familiar with frameworks like LangChain, Transformers, or Hugging Face datasets.
- Understanding of vector embeddings and experience with vector search tools.
- Strong grasp of model deployment workflows (e.g., Docker, REST APIs, cloud services like AWS/Microsoft Azure).
- Familiarity with prompt engineering strategies and LLM behavior tuning.
- Experience integrating with APIs from OpenAI, Anthropic, or open-source models like Mistral or LLaMA.
Preferred Qualifications
- Experience fine-tuning models using PEFT methods (LoRA/QLoRA).
- Familiarity with LLMOps, observability, and model evaluation?
- Exposure to security, privacy, and responsible AI practices.
- Prior experience building chatbots, copilots, document analyzers, or other AI apps.
- Contributions to open-source AI tools or GenAI workflows.