Senior AWS / Python Engineer for Distributed Lab Automation & AI-Enabled Data Platform

Senior AWS / Python Engineer for Distributed Lab Automation, High-Performance Data Analysis, and Agentic AI Application We are seeking a highly skilled senior software engineer to help design and build a distributed laboratory automation and data-analysis platform. This is not a simple lab equipment integration project. The goal is to create a robust application that connects desktop-based laboratory software, technician workflows, calibration procedures, data pipelines, cloud infrastructure, and eventually agentic AI capabilities into one coordinated system. Project Overview We need to build a central manager application that allows lab managers to create calibration orders, define order specifications, assign or track work, and coordinate execution across technician workstations and existing laboratory software. A calibration technician will use a local desktop-based Python application to select an assigned order, review the required specifications, and start the calibration workflow. Once started, the application will send commands to existing laboratory software and instruments, collect measurement data, analyze the results, detect steady-state behavior in temperature measurements, calculate calibration offsets, and support reporting or visualization. The system should also have a cloud-connected architecture, allowing order management, data storage, reporting, and future AI-assisted workflows to be managed centrally. Core Responsibilities The selected engineer will help architect and implement a distributed application involving: * A central manager node for creating, managing, and tracking calibration orders * A local Python desktop application that wraps and controls existing laboratory software * Communication between local lab workstations, manager services, and cloud infrastructure * AWS-based backend services for order management, data storage, authentication, APIs, and reporting * Data ingestion, analysis, visualization, and calibration offset calculation * Algorithms for identifying steady-state regions in temperature and sensor measurement data * Reliable orchestration of commands sent to existing lab software and equipment * Cloud/local synchronization, error handling, logging, and auditability * Future extensibility for generative AI and agentic AI features Technical Focus Areas This project requires strong engineering judgment across several domains: AWS Cloud Architecture We expect the system to use modern cloud-native patterns where appropriate, potentially including services such as ECS, Lambda, API Gateway, S3, RDS/Postgres, DynamoDB, SQS, EventBridge, IAM, CloudWatch, and infrastructure-as-code. The ideal candidate should be comfortable designing reliable, secure, maintainable AWS architectures for real-world business applications. Python Application Development The local lab application will likely be Python-based and may need to interface with desktop software, command-line tools, files, APIs, local services, instruments, or vendor-specific applications. Experience with Python automation, desktop applications, process orchestration, device/software control, and resilient error handling is highly valuable. High-Performance Data Analysis and Visualization The platform will process laboratory measurement data, including temperature data and calibration results. The application should support analysis, visualization, and automated determination of calibration offsets. Relevant experience may include NumPy, Pandas, SciPy, Plotly, Matplotlib, signal processing, time-series analysis, statistical methods, and scientific or industrial data workflows. Agentic AI and Generative AI We are also interested in adding practical AI features where they create real value. Possible examples include: * AI-assisted review of calibration results * Automated explanation of failed or unusual calibration runs * Natural-language querying of historical calibration data * Agentic workflows for generating reports or recommending next actions * AI-assisted troubleshooting based on logs, measurement patterns, and technician notes * Intelligent summarization of orders, deviations, and calibration outcomes * Retrieval-augmented search over lab procedures, manuals, historical results, and SOPs We do not want AI features added as a gimmick. We want thoughtful implementation where AI meaningfully improves the workflow, reduces technician burden, improves quality control, or helps managers understand lab performance. Ideal Candidate The ideal candidate will have experience with several of the following: * Senior-level Python development * AWS cloud architecture and backend application development * Distributed systems or cloud-connected local applications * Desktop automation or local software integration * Scientific, laboratory, industrial, sensor, or measurement-data applications * Time-series data analysis and visualization * Calibration, instrumentation, test automation, or hardware/software integration * FastAPI, REST APIs, event-driven architecture, queues, and background workers * PostgreSQL, DynamoDB, S3, or other structured/unstructured data stores * Secure authentication, logging, monitoring, and deployment practices * Generative AI, agentic AI, RAG, LangChain, LlamaIndex, Bedrock, OpenAI, Anthropic, or similar tools Project Scope This will likely be a phased project. Initial work may include architecture, system design, proof-of-concept development, and defining the communication pattern between the manager node, local technician application, lab software, and cloud backend. Later phases may include productionizing the desktop application, building the cloud backend, implementing calibration data analysis, creating dashboards and reports, and adding AI-assisted workflows. What We Are Looking For We are looking for someone who can think like an architect, build like a senior engineer, and communicate clearly about tradeoffs. This project will require more than basic coding. We need someone who can help design a reliable system that works in a real laboratory environment, handles edge cases, preserves data integrity, and can grow into a serious cloud-connected automation platform. Please apply with examples of relevant work involving AWS, Python automation, distributed applications, scientific data analysis, laboratory software, industrial systems, or AI-enabled applications. In your response, please briefly describe how you would approach the architecture for a system that connects local lab software, technician workflows, data analysis, and cloud-based order management.