Job Opportunities API

Role Overview We are looking for a forward-thinking Senior AI Developer to architect and build the next generation of Agentic AI applications in the healthcare industry. You will move beyond standard prompt engineering to design Agentic Workflows—systems where LLMs function as reasoning engines that can plan multi-step tasks, utilize external tools (APIs, databases), and collaborate with other specialized agents to achieve high-level goals. You will contribute to the technical implementation of "Agency" in our software, transforming passive AI responses into active, goal-oriented behaviors. Experience working with the GCP Vertex AI ecosystem is a big plus. In this role, you will design agents capable of complex reasoning and tool usage, leveraging Google’s managed infrastructure (Vertex AI Vector Search, Cloud Run, and BigQuery) to ensure reliability, security, and performance. Requirements Key Responsibilities 1. Architecting and Implementing Agentic Workflows Design Autonomous Loops: Build stateful control loops (Perception ->  Reasoning -> Action) where agents can interpret clinical data, plan necessary checks, and generate risk assessments. Conversational Agentic workflow: Chatbot style user experience powered by LLM GCP Integration: Leverage Vertex AI for model serving and LLM models for reasoning, optimizing for long-context windows to process complex medical history. RAG & Grounding: Implement "Enterprise Grounding" using Vertex AI Vector Search to ensure all agent outputs are strictly cited against official medical literature (e.g., medical research and guidelines). 2. Data & Tool Integration Structured Data Access: Build robust Python tools (Function Calling) that allow agents to securely query BigQuery or other datastores for patient demographics and symptoms. Security & Compliance: Ensure all AI operations comply with HIPAA/GDPR standards, implementing strict PII masking and data governance within the Google Cloud environment. System Reliability: Deploy agents on Vertex AI, Cloud Run, or GKE, ensuring low-latency inference and high availability for clinical users. Agent monitoring: implement effective product environment AI Agent execution monitoring and alert system to ensure SLA, safety and security compliance 3. Leadership & Strategy Mentorship: Guide a team of engineers in MLOps best practices (CI/CD for models, evaluation pipelines). Evaluation: Implement "LLM-as-a-Judge" frameworks to automatically test agent accuracy against "Golden Datasets" of clinical cases. Required Technical Skills Core AI: Deep proficiency with LangChain or LangGraph, and specific experience with the Vertex AI SDK and ADK (Agent Development Kit) is highly desired LLMs: Expertise in prompting and configuring LLMs, specifically utilizing Function Calling and Context Caching. Experience with integrating LLMs into platform and application workflow, experience with MCP protocols RAG/Embeddings: expert level experience in design and implementation of RAG based system using embeddings and vector databases Data Engineering: Strong SQL skills for BigQuery; experience with Vector Databases (Pinecone, Milvus, or Vertex Vector Search) Backend Engineering: Mastery of Python (FastAPI) and containerization (Docker) for deploying microservices to Cloud Run or GKE. Healthcare Domain: Familiarity with handling unstructured medical text or clinical guidelines is highly preferred. Qualifications Experience: 5+ years of software development experience. 2+ years of hands-on experience building and deploying enterprise-grade agentic applications (e.g., using LangGraph, LangChain). Familiarity with scalable, enterprise-level distributed systems Experience with advanced data structures, algorithms, and complexity analysis Demonstrated history of product value delivery Education: Bachelor's degree in Computer Science, Computer Engineering, or a closely related quantitative field