Lead Python Developer (Local to IL)

Lead Python Developer
Schaumburg, IL (Locals only)

Hybrid - 3 days/week onsite

Role Summary

We are seeking a collaborative, detail-oriented, and motivated Software Engineer to join our engineering team. In this role, you will work alongside senior engineers to design, build, test, and maintain robust software services, data pipelines, and backend components that power intelligent enterprise applications.

Your core focus will center on software development, developing of scripts to automate business and engineering workflows, data engineering, automated testing and continuous system improvement. You will write modular, maintainable code, implement comprehensive unit and integration test suites, support database and cloud operations, and help integrate backend systems with emerging AI-enabled capabilities. This is an excellent opportunity for an engineer with strong foundational software skills who is excited to grow and optionally gain hands-on exposure to modern AI platforms. 

Key Responsibilities

1. Backend Engineering & Service Integration

• Core Application Development: Partner with senior engineers to implement, refine, and debug backend logic, microservices, and API integrations.
• Scripting & Workflow Automation: Develop clean, modular Python scripts and utilities to automate data ingestion, batch processing jobs, and repetitive engineering workflows.
• API & System Connectivity: Build and consume RESTful APIs and modern communication protocols to interface with internal tools and third-party services.
• UI & Integration Support: Assist with lightweight frontend or dashboard integrations (e.g., React or Python-based internal UI tools) to facilitate internal testing and demos.

3. Data Engineering & Cloud Operations

• Database Querying & Transformation: Write efficient SQL queries to extract, transform, inspect, and validate datasets across relational databases (e.g., PostgreSQL, MS SQL Server, MySQL) and enterprise cloud warehouses.
• Cloud Platform Support: Support the configuration, deployment, and monitoring of backend microservices and data pipelines within cloud environments (e.g., Google Cloud Platform, AWS, or Azure).
• Observability & Monitoring: Help monitor application performance, track error rates, inspect service logs, and measure resource/API usage to optimize system efficiency. 

3. Software Quality, Testing & Validation

• Automated Testing: Design, write, and maintain automated test suites (unit, integration, regression, and performance tests) using standard frameworks such as pytest.
• System Reliability & Benchmarking: Implement testing scripts to benchmark API latency, validate system outputs, and verify regression stability across service updates.
• Test Data Management: Curate, sanitize, and manage structured test datasets and mock environments for rigorous edge-case testing.
• Quality Assurance: Participate in code quality initiatives, adhering to clean architecture, style guidelines, and robust error-handling standards. 

4. Collaboration & Continuous Improvement

• Agile Participation: Actively participate in sprint planning, stand-ups, technical brainstorming sessions, and peer code reviews.
• Technical Documentation: Maintain clear, comprehensive documentation, including runbooks, API specifications, setup guides, and architectural notes.
• Professional Growth: Continuously broaden software engineering skills, keep up with industry best practices, and explore emerging technologies. 

Required Qualifications

• Education: Bachelor’s or master's degree in computer science, Software Engineering, Data Science, or a related technical field (or equivalent practical experience).
• Experience: 2 to 3+ years of professional, internship, or open-source software engineering experience with a strong foundation in Python.
• Programming Proficiency: Strong command of Python, including object-oriented programming, asynchronous scripting, data manipulation (e.g., pandas, numpy), and standard software design patterns.
• Testing & Quality Mindset: Demonstrated experience writing and maintaining automated test suites using frameworks such as pytest or unittest.
• Database & SQL: Proficiency in writing SQL queries to retrieve, validate, and manipulate structured data in relational databases (e.g., PostgreSQL, MS SQL Server, MySQL) or cloud data warehouses.
• Cloud & Infrastructure Exposure: Foundational hands-on experience or familiarity with cloud platforms (Google Cloud Platform preferred—e.g., BigQuery, Cloud Run—or equivalent services in AWS/Azure).
• Problem-Solving & Communication: Strong analytical thinking, attention to detail, and the ability to articulate technical findings clearly to peers and cross-functional partners.
• Professional Collaboration: Ability to take ownership of assigned tasks, work effectively within a distributed engineering team, and actively contribute to team deliverables.
• Frontend Prototyping: Experience with modern web technologies (e.g., React), Python Fast API or lightweight rapid-prototyping libraries (e.g., Streamlit, etc.).
• DevOps & CI/CD: Practical understanding of CI/CD pipelines (e.g., GitHub Actions, GitLab CI) and containerization using Docker.
• AI: Understands AI concepts and development, basic LLM know-how and ability to work with GitHub copilot and AI based development tools 

Preferred Qualifications

• AI & Machine Learning Concepts: Working knowledge of Generative AI principles, prompt engineering, LLM APIs, and core agentic concepts (e.g., RAG pipelines, embeddings, semantic search, tool use/function calling).
• Agentic Frameworks: Hands-on familiarity with agentic frameworks and orchestration libraries (e.g., LangChain, LangGraph, LlamaIndex, CrewAI, or Google Agent Builder/ADK).
• LLM Evaluation & Validation: Familiarity with evaluation frameworks and metric tracking for AI outputs (e.g., Ragas, DeepEval, Promptfoo, or custom eval harnesses).
• Protocols & Advanced Integrations: Experience with Model Context Protocol (MCP) or integrating external tools and REST APIs into automated agent workflows.
• Safety & Guardrails: Exposure to synthetic data generation, red-teaming, toxicity filtering, or hallucination detection guardrails.