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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, AI & Data - **Company:** RENTERRA LLC - **Location:** Chicago, IL, United States (Remote available) - **Experience:** Expert - **Salary:** $150,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audit Trail, Databases, Data Architecture, Data Deduplication, Data Governance, Data Structures, Data Systems, Machine Learning, Raw Data, Search Technologies, SQL Databases, Large Language Models, Apache Spark, Change Data Capture, Event Driven Architecture, Apache Flink, Apache Kafka, Database Replication, Vertica, Data Pipelines - **Published:** July 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=1524ed51190ca8a3 ## About the Role You're an AI-forward builder who wants to own the full path from raw data to a customer-facing product that solves a real problem. You're a high-caliber software engineer at the Senior, Staff, or Principal level. You've built and operated production systems and bring strong product and architectural judgment. You have deep experience with data architecture, SQL, and data modeling, including analytical databases such as ClickHouse. You understand how data structure and access patterns affect performance, maintainability, and cost. You understand distributed and event-driven systems, including ordering, retries, deduplication, replay, schema evolution, and failure recovery. You've shipped AI- or ML-powered product features and understand the full system around the model: data, retrieval, tool use, evaluation, observability, permissions, and fallbacks. ## Description We value velocity and efficiency through fast iteration cycles. Our lean team doesn't sit on ceremony. Build times, deployments, and meetings are kept tight so we can deliver effective solutions to our customers. We expect engineers to own outcomes, make pragmatic architectural decisions, and stay close to the people using what they build. What you'll do Build our data and AI foundation: Design how operational and financial data is captured, structured, processed, stored, and queried across Renterra. This includes event architecture, data pipelines, and analytical systems such as ClickHouse. Create AI-powered products: Build agents and intelligent workflows that use Renterra's data to identify problems, answer questions, support decisions, and automate work for our customers. Own production quality: Make data and AI systems reliable, observable, secure, and cost-effective. Design for failure recovery, data quality, permissions, model uncertainty, and appropriate human review. Measure what works: Develop evaluations and feedback loops for accuracy, reliability, latency, cost, and customer impact. Use what you learn to improve both the underlying data systems and the product experience. Work across the company: Partner with leadership, product, engineering, support, and customers to find valuable problems and turn them into shipped solutions. Make pragmatic technical decisions: Choose the right approach for each problem, whether that is a deterministic workflow, analytical query, machine-learning model, LLM, vendor, or a combination of them. Report directly to the CTO., * Building agentic systems that call tools or take actions in production * Retrieval, embeddings, semantic search, or hybrid search * Evaluation frameworks for AI agents and LLM-powered features * Classical machine learning, forecasting, anomaly detection, or optimization * Kafka, Redpanda, Flink, Spark, or similar event-processing systems * Change data capture and database replication * Data governance, permissions, privacy, and auditability * Financial, accounting, logistics, or other operational business data * Customer-facing analytics and reporting products ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Bringing Clarity to Event Streams: Enabling Analytics and AI Through Rich Metadata](https://www.wearedevelopers.com/videos/1616-bringing-clarity-to-event-streams-enabling-analytics-and-ai-through-rich-metadata) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [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) - [Trustworthy AI Starts at Deployment: 5 Checks Before You Ship](https://www.wearedevelopers.com/magazine/753-trustworthy-ai-starts-at-deployment-5-checks-before-you-ship) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)