Sr Software Engineer, SmartSense (Boston, MA - Hybrid)

Security People•Boston, MA

43d•$103,000 - $161,500•Hybrid

About The Position

Join a high-performing, tight-knit team at a fast-growing company that is using the Internet of Things (IoT) and Advanced Analytics to transform how organizations sense, monitor, and make decisions. Founded out of MIT in 2005, SmartSense by Digi is trusted by more than 2,000 organizations — including Walmart, SpaceX, Apple, CVS Health, Coca-Cola, and the US State Department — to help them make sensor-driven, data-informed decisions. Our platform empowers customers to make mission-critical decisions every day. As we continue to advance it, we're integrating Advanced Analytics, Machine Learning (ML), and Generative AI (GenAI) into our connected sensing ecosystem. We're looking for a Software Engineer who excels at the intersection of data, AI, and scalable systems — someone capable of building robust pipelines, deploying models, and transforming insights into intelligent, user-facing applications. What We Offer This is an exciting opportunity for a Senior Software Engineer who wants to help drive our vision for an open, intelligent sensing platform. You'll join a dynamic Analytics team of engineers expanding SmartSense's capabilities — from backend data pipelines and cloud AI services to interactive front-end applications that bring insights directly to our customers. You'll help shape SmartSense's AI-driven future: developing analytics platforms, orchestrating model workflows, and building applications that enable real-time, data-informed decision-making.

Requirements

5+ years of related experience with a Bachelor's degree in Engineering discipline, or equivalent; or 3-4 years and a Master's degree; or equivalent work experience.
Deep understanding of cloud technologies — including AWS, GCP.
Strong programming skills in Python, SQL, JavaScript
Hands-on experience with REST API Frameworks (e.g., FastAPI, Flask) and API design including Row Level Security, OpenAPI/Swagger documentation, endpoint versioning.
Solid grasp of CI/CD using Argo Helm, Jenkins, and automated test pipelines (pytest).
Hands-on Experience with containerized orchestration (Docker, Kubernetes, Helm, Argo).
Experience with React front-end applications.
Experience with model deployment and monitoring (Vertex AI, SageMaker, etc.).
Familiarity with Model Context Protocol (MCP) and GenAI applications and deployment
Familiarity with data pipelines, ETL and lakehouse and both SQL and NoSQL databases, including Snowflake, MSSQL, PostgreSQL, MongoDB, AWS DocumentDB database
Excellent communication, attention to detail, and commitment to follow-through.

Nice To Haves

Experience operating high-availability, 24x7 production systems and monitoring them for performance and reliability.
Strong understanding of authentication and authorization frameworks (OAuth2, JWT, API keys).
Familiarity with HTML, CSS for front development.
Experience building real-time or streaming analytics systems (Kafka, Redpanda).
Knowledge of CloudEvents, Protobuf, or AVRO for event-driven architecture.
Experience integrating orchestration and automation tools (n8n, Zapier, Airflow).
Experience in healthcare, food service, grocery or education industries, or an understanding of the regulatory requirements around them.
Experience in software development of IoT contexts
Experience implementing and testing stream processing applications in isolated development environments

Responsibilities

Design, develop and maintain React-based and BI platform applications that deliver intelligent insights to customers and internal teams.
Develop and maintain secure and scalable APIs and microservices to integrate models, analytics, and data pipelines.
Implement and deploy ML and Generative AI models and services across the SmartSense platform.
Develop and extend Model Context Protocol (MCP) components to tailor LLM to SmartSense use cases.
Implement CI/CD pipelines using Argo Helm, Jenkins, and GitHub Actions, ensuring automated deployment and reliability.
Create and maintain automated testing frameworks with pytest and related tools such as Testim and Cypress.
Manage the model lifecycle — from experimentation and training to deployment and continuous monitoring — using cloud-native services.
Operate across multiple cloud environments (AWS, GCP, and Snowflake) to ensure reliability, scalability, and cost efficiency.
Collaborate with data, platform, and application teams to deliver high-quality, production-ready solutions.
Maintain rigorous documentation and champion engineering excellence through code reviews and best practices.