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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer - **Company:** Anova LLC - **Location:** New Providence, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Analysis of Variance (ANOVA), Unit Testing, Code Review, Continuous Integration, Information Engineering, Cursor (Graphical User Interface Elements), Python (Programming Language), PostgreSQL, Queueing Systems, RabbitMQ, Software Deployment, Software Engineering, Software Systems, SQL Databases, Snowflake, Backend, Git, Pandas, Pyspark, Information Technology, Apache Kafka, Virtual Agents, Data Pipelines, Databricks - **Published:** September 21, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/plrdg0c7jp ## About the Role * Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, or another quantitative or scientific field - or an equivalent combination of education and experience. * 5+ years in software engineering or a closely related role, including hands-on production deployment (6-8 years preferred). A completed PhD in a quantitative or scientific discipline, with substantial hands-on programming, counts toward this. * Strong Python and the engineering habits that go with it: git, code review, linters, unit tests, and CI/CD pipelines are things you use daily. * Strong analytical ability and working fluency with statistics: distributions, uncertainty, correlation and causation, and knowing when a result is real. * Solid SQL and experience working with data at scale - pandas, PySpark, and platforms such as Databricks, Snowflake, or Postgres. * Experience operating software in production: deployment, monitoring, alerting, and a feel for the practices that make that sustainable. * Fluency with agentic coding tools. * Fluent in written and spoken English. Preferred Qualifications - * Depth in the Azure Databricks platform: PySpark, streaming pipelines. * Time series work in production: forecasting, anomaly detection, or similar. * Experience with message queuing systems (Kafka, RabbitMQ, or similar). * Experience implementing agentic workflows in production - building them, not just calling them. * Hands-on with an agent framework: LangChain / LangGraph, CrewAI, AutoGen, MetaGPT, or similar. We care that you have shipped one, not which one. * Familiarity with the interoperability protocols - MCP (Model Context Protocol) and A2A (Agent2Agent) - for exposing systems as tools other agents can call directly, and for getting agents to talk to each other. * Experience with Microsoft Foundry for building, evaluating, and deploying agents. * Domain experience in industrial, energy, or IoT settings. ## Description Where You'll Work: This is a remote-friendly role - most of your work in practice can be done remotely, with occasional in-person collaboration at our Porto office. Our engineers tell us that flexibility is one of the things they value most about working here. Job Duties and Responsibilities: Reporting to the Digital Integration Director, you will own software solutions end to end, from framing the business problem to running services reliably in production, built on real-time telemetry from industrial IoT sensors monitoring liquid and gas storage tanks across industries including industrial chemical, industrial gas, propane, and other fuels. Collaborate for success * Own projects end to end, starting from a greenfield state: define your own requirements, shape the roadmap, and take solutions through to production. * Work cross-functionally with teams like Sales and Marketing, translating business goals into working software. * Make the technical decisions and contribute significantly to the implementation - this is a hands-on role, not a hand-off-to-others one. Build data-driven systems * Build and operate the backend services and APIs behind a platform ingesting real-time telemetry from industrial IoT sensors at sites across the globe. * Design and run the data pipelines that turn that telemetry into something people can act on - including forecasting and anomaly detection on time series. * Work with the realities of sensor data: gaps, drift, irregular sampling, and a device population that keeps evolving. Bring the statistical judgment to tell a real signal from noise, and to say how confident we should be. * Run what you build - instrumentation, alerting, and the operational habits that keep a system healthy. Engineer with AI assistance * Use agentic coding tools - Claude Code, Copilot, Cursor, and similar - as a normal part of daily delivery. * Hold AI-generated code to the same bar as any other code. You are accountable for what you ship. * Structure repositories, tests, and documentation so both people and agents can work in them effectively, and share the patterns and guardrails that work so the team's baseline rises. * Apply Anova's AI Handbook guidance on risk and human-in-the-loop validation to any system whose output reaches a customer or drives an automated action. Advocate for quality * Contribute to and continuously adapt best practices and Ways of Working across software engineering, data engineering, and platform operations, so the team ships high-quality solutions that create real impact for our clients. What Success Looks Like * Backend services and data pipelines for real-time IoT telemetry run reliably in production, with strong instrumentation and alerting in place. * Forecasting and anomaly detection outputs are trustworthy enough that customers and internal stakeholders act on them with confidence. * Engineering practices - code review, testing, CI/CD, AI-assisted development - visibly raise the team's baseline, not just this person's own output. * Other engineers are actively mentored and leveled up through code review and design discussion. * Business stakeholders trust this person to translate technical trade-offs and uncertainty into decisions they can act on. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Building a Multi-Agent Orchestration Engine That Actually Follows the Rules](https://www.wearedevelopers.com/videos/100159-building-a-multi-agent-orchestration-engine-that-actually-follows-the-rules) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [The 12 Best Jobs for Software Engineers](https://www.wearedevelopers.com/magazine/401-the-12-best-jobs-for-software-engineers)