Senior Software Engineer - AI

About this role

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Skills you'll use

What you'll do

• Design and reason with agentic AI frameworks — experienced in building multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, integrating reasoning, reflection, planning, tool use and memory to create intelligent, goal-driven systems. Have a deep understanding of capabilities of multi-modal models in agentic systems.
• Understand evaluation deeply — fluent in ML validation and measurement (Precision, Recall, Specificity, NPV, etc.), and experienced designing reward functions, evaluators, or grader models for reinforcement fine-tuning and continuous improvement. Good understanding of LLM as judges for qualitative and semantic evaluations.
• Fluency in embeddings and vector stores — experienced in building or integrating retrieval-augmented generation (RAG) pipelines, managing vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search, or similar), and leveraging semantic search or context injection to enhance reasoning.
• Adapt and learn continuously — staying ahead of evolving AI architectures, agentic frameworks, and emerging paradigms in reasoning and retrieval. You have curiosity, humility, and a bias for iteration.
• A solid understanding of classical ML, and LLM fundamentals — knowledgeable in transformer architectures, attention mechanisms, and tokenization principles. You understand how embeddings, context windows, and model scaling laws influence quality, cost, and performance.
• Excellence in model fine-tuning — with hands-on expertise fine-tuning large language models (LLMs) or multimodal models using supervised, reinforcement, or instruction-tuning techniques (e.g., LoRA, PEFT, TRL). Skilled at optimizing model efficiency, interpretability, and inference performance.
• Collaboration across boundaries — partnering with platform, application, and product engineers to transform concepts into scalable, reliable AI solutions. You communicate clearly, share knowledge openly, and thrive in cross-functional teams.
• Solid engineering — grounded in strong software design principles, modular architecture, and code quality. You can translate experimental ideas into robust, maintainable systems that integrate seamlessly within large-scale AI platforms.

Who can apply

• Demonstrating ownership — capable of setting technical direction, mentoring peers, and making pragmatic design trade-offs that balance innovation, performance, and reliability.
• Acting with purpose — applying thoughtful engineering and ethical AI principles to create systems that advance healthcare intelligence responsibly and at scale.
• B.S. or M.S. in Computer Science, Artificial Intelligence, or a related technical field.
• 5+ years of industry experience building and deploying scalable, production-ready ML systems in a collaborative engineering environment.
• Strong software engineering fundamentals, with proficiency in Python and experience designing scalable systems in modern cloud environments (Azure, AWS, or GCP).  Experienced in modern development workflows such as code reviews, CI/CD pipelines, and collaborative development tools. Experience with debugging, testing, feature flags, experimentation and version control (Git). Exposure to Python-based web services (FastAPI a plus) and containerized application development (Docker or Kubernetes).
• Proficiency with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI) and understanding of interoperability protocols such as MCP and A2A. Deep experience with MCP (Model Context Protocol) architecture, AI systems or APIs, or large-scale codebases is preferred.
• Experience working with vector databases and embeddings, integrating FAISS, Pinecone, Chroma, or Azure AI Search into retrieval-augmented generation (RAG) or semantic-search pipelines.
• Hands-on experience fine-tuning and optimizing large language models (LLMs) or multimodal models, using techniques such as LoRA, PEFT, and TRL.

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