Senior AI/LLM Engineer

Description We are seeking a highly skilled AI/LLM Application Engineer to build and deploy intelligent applications for the healthcare sector. This role requires a strong software development background and deep expertise in developing production-level applications with Large Language Models (LLMs). The ideal candidate will excel at building agentic workflows and multi-modal data integration pipelines for clinical applications, with a focus on optimizing application performance. A strong commitment to patient privacy, security, and ethical AI is essential. Responsibilities: Design, develop, and deploy robust, LLM-powered applications tailored for healthcare, such as clinical decision support tools and automated document processing. Design and deploy agentic workflows for clinical applications where AI agents autonomously execute multi-step clinical tasks by orchestrating tools, adapting to dynamic contexts, and integrating diverse data modalities. Architect and implement robust data integration pipelines that fuse complex, multi-modal clinical information—including documents (PDFs, images), unstructured clinical narratives, and semi-structured medical data—into unified workflows that enable LLMs to analyze, contextualize, and generate actionable clinical insights. Apply advanced data retrieval methods to ground LLM-generated responses in specialized healthcare knowledge. Write high-quality, efficient, and well-documented Python code for the entire application lifecycle, from data handling to model serving. Engineer features and preprocess medical datasets to ensure data quality and model readiness. Profile and optimize the performance of data workflows and application code for efficient processing. Implement and enforce MLOps/LLMOps best practices, including version control, CI/CD pipelines, and performance monitoring, to ensure system reliability and compliance. Ensure strict compliance with healthcare regulations (e.g., HIPAA) and ethical AI principles throughout the development process. Collaborate with cross-functional teams, including clinicians, product managers, and other engineers, to define requirements and deliver impactful solutions. Stay up-to-date with the latest advancements in LLMs and generative AI. Requirements Required Skills and Qualifications: Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related technical field. Programming Expertise: Proficient in Python programming with proven experience developing production-grade applications, including robust data processing pipelines, API integration, error handling, and performance optimization. Data Analysis & Engineering: Strong data analysis capabilities with experience in exploratory data analysis (EDA), statistical reasoning, feature engineering, and deriving actionable insights from complex datasets. Proficiency with data manipulation libraries (pandas, numpy) and visualization tools is required. LLM Applications (Required): Minimum 2+ years of hands-on experience developing and deploying production-level applications using LLMs and generative AI, with demonstrated ability to process and engineer features from multi-modal clinical data sources. Experience with prompt engineering, fine-tuning, and LLM evaluation techniques is highly valued. Agentic Workflows (Required): Proven track record building and deploying agentic workflows and multi-agent systems in production environments, using frameworks such as Agno, CrewAI, LangGraph, or similar tools. Multi-Modal Data Integration: Practical experience working with diverse data modalities, including structured, semi-structured, and unstructured data (documents, images, clinical narratives, lab results, medication records). Healthcare Domain: Understanding of clinical workflows and regulatory constraints (HIPAA) in healthcare environments. Soft Skills: Exceptional problem-solving abilities, strong communication skills to bridge technical and clinical teams, and meticulous attention to detail crucial for healthcare applications. Preferred Qualifications: Experience developing with AI-powered software development tools (e.g., Claude-code, Cursor). Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). Knowledge of conversational AI platforms and tools for building clinical-grade conversational agents. Familiarity with medical data standards (e.g., FHIR)