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# Senior Software Engineer

- **Company:** BrickRed Systems
- **Location:** Medina, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Microsoft Azure, Cloud Computing, Encodings, Continuous Integration, Information Engineering, Python (Programming Language), Mesh Networking, Network Administration, Rapid Prototyping Process, Prometheus, Azure Data Lake, Software Engineering, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Snowflake, Grafana, Agentic-AI, AWS ECS, Fastapi, Build Management, Amazon Relational Database Service, Containerization, Data Lakes, AI Platforms, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Low Latency, HuggingFace, AWS Fargate, OSS/BSS, Data Management, Ollama, Machine Learning Operations, Restful APIs, Amazon Simple Queue Service (SQS), Terraform, Docker, Key Vault, Databricks, Microservices
- **Published:** October 2, 2026
- **Apply:** https://www.disabledperson.com/jobs/75679879-senior-software-engineer

## About the Role

We are looking for a Senior Software Engineer - Lakehouse & Applied AI with strong hands-on experience in Databricks, PySpark, Python, cloud infrastructure, and GenAI/LLM technologies.

The ideal candidate will combine data engineering and software engineering expertise with experience building production-grade AI/LLM solutions, cloud platforms, infrastructure-as-code, and containerized workloads.

This role involves owning solutions end-to-end, from discovery and rapid prototyping through production deployment, while working directly with stakeholders in a forward-deployed environment., * 6+ years of experience in software/data engineering.

 * Strong hands-on experience with Databricks, PySpark, Delta Lake, and Python.
 * Experience with Medallion Architecture, Auto Loader, Unity Catalog, and Databricks Asset Bundles.
 * Hands-on experience with GenAI, LLMs, AI agents, RAG, embeddings, or agentic workflows.
 * Working knowledge of MCP or similar LLM tool-serving frameworks.
 * Strong experience with Terraform and Terragrunt.
 * Experience across Azure and/or AWS cloud environments.
 * Hands-on experience with Docker, Kubernetes, ECS/Fargate, and CI/CD.
 * Strong Python development skills with FastAPI, Flask, REST APIs, and microservices.
 * Experience with Airflow or similar workflow orchestration tools.
 * Knowledge of Prometheus, Grafana, or similar observability platforms.
 * Strong problem-solving skills with the ability to own solutions from prototype to production.
 * Comfortable working in a customer-facing, forward-deployed engineering environment.

Preferred Qualifications

 * Experience with local/on-premises LLM deployment using Ollama, Llama, Hugging Face, or similar technologies.
 * Experience working with Snowflake alongside Databricks.
 * Experience building multi-tenant data or AI platforms.
 * Telecommunications experience involving 3G/4G-LTE OSS/BSS, network management, backhaul, or 5G.
 * Experience with AI-assisted software engineering and autonomous/agentic development workflows.
 * Experience optimizing production systems for latency, reliability, scalability, and cost.

## Description

* Design and deliver end-to-end lakehouse and applied-AI solutions from prototype through production.
 * Build scalable data platforms using Databricks, PySpark, Delta Lake, and Azure.
 * Develop agentic AI workflows and tool-using AI agents for software and infrastructure automation.
 * Build AI solutions using LLMs, embeddings, RAG, and MCP or similar tool-serving frameworks.
 * Develop reusable multi-tenant CI/CD and deployment frameworks.
 * Build and deploy local/edge LLM and embedding-serving environments.
 * Develop Python-based REST APIs and microservices using FastAPI, Flask, or similar frameworks.
 * Deploy and orchestrate production workloads using Docker, Kubernetes, ECS/Fargate, and Airflow.
 * Manage cloud infrastructure using Terraform and Terragrunt across Azure and AWS.
 * Work with services including Azure Databricks, ADLS Gen2, Key Vault, AWS ECS, S3, RDS, SQS, and SSM.
 * Develop AI evaluation and testing frameworks covering regression detection, retrieval quality, task completion, and safe rollback.
 * Implement monitoring and observability using tools such as Prometheus and Grafana.
 * Work directly with customers and stakeholders to translate ambiguous requirements into production-ready technical solutions.