AI Engineer

Job Summary

We are seeking an AI Engineer with hands-on experience in Large Language Models (LLMs), generative AI systems, and applied Natural Language Processing (NLP) for healthcare use cases. This role will focus on building, integrating, optimizing, and evaluating AI models and pipelines, ensuring accuracy, scalability, and compliance in regulated healthcare environments.

Responsibilities

• AI Model Development & Optimization

  • Design, build, and optimize LLM/SLM-powered AI systems for healthcare applications.
  • Develop and refine model interaction layers (prompting, orchestration, retrieval, and reasoning pipelines).
  • Improve model accuracy, latency, coherence, and safety through iterative experimentation.

• LLM Training, Fine-Tuning & Customization

  • Collaborate with ML/NLP teams to fine-tune and adapt LLMs and SLMs for domain-specific healthcare tasks.
  • Assist in data curation, augmentation, preprocessing, and evaluation to enhance model performance.
  • Integrate RAG (Retrieval-Augmented Generation) and memory-based architectures where applicable.

• Generative & Conversational AI Systems

  • Build AI-driven workflows for healthcare conversations, summarization, decision support, and automation.
  • Ensure conversational outputs meet clinical relevance, compliance, and safety standards.

• Evaluation, Monitoring & Continuous Improvement

  • Define and implement evaluation metrics and testing frameworks to measure AI system performance.
  • Conduct A/B testing and monitoring to iteratively improve AI behavior based on real-world usage.
  • Incorporate user and clinician feedback to continuously refine outputs.

• Collaboration & Research

  • Work closely with healthcare professionals, product teams, data engineers, and software engineers.
  • Stay updated with the latest advancements in LLMs, generative AI, NLP, and applied AI research.
  • Translate research insights into scalable, production-ready AI solutions.

Qualifications

• Generative AI & LLMs: Hands-on experience with GPT models, LLaMA, or other open/closed-source LLM architectures.
• AI Engineering: Experience building end-to-end AI pipelines and production-grade AI systems.
• NLP Expertise: Strong understanding of semantic search, intent recognition, contextual AI, and RAG.
• Programming: Proficiency in Python, with experience using LangChain, Transformers, or similar frameworks.
• Evaluation & Experimentation: Experience with A/B testing, performance benchmarking, and model evaluation.
• Healthcare Domain (Preferred): Familiarity with healthcare workflows, clinical documentation, or medical terminology.