Lead Software Engineer
ChicagoAll locationsChicagoIrvinePhoenixSan FranciscoSeattleLos AngelesSan JoseDenver DowntownSan DiegoSalt Lake City Hybrid
$126,500–$223,900 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
The Lead Software Engineer is a hands-on technical leader responsible for designing and delivering modern, cloud-native software platforms that support complex business workflows, data-intensive applications, and AI-enabled capabilities. This role requires strong engineering depth, architectural judgment, and the ability to translate ambiguous business and domain requirements into scalable, secure, observable, and maintainable technical solutions. The individual will guide teams across distributed systems, APIs, data pipelines, workflow orchestration, configuration-driven rules, and retrieval-augmented AI patterns while ensuring solutions are governed, traceable, reproducible, and aligned to enterprise engineering standards.
What you'll bring
- age
- 5–7 years of experience in software engineering, with at least 2 years in a leadership or architectural capacity.
- Experience managing teams and delivering solutions in complex or regulated environments.
- Deep expertise in modern programming languages and frameworks such as Java, C#, Python, JavaScript/TypeScript, and Rust.
- Proficiency with databases (SQL, NoSQL) and API design/integration.
- Experience with cloud-native applications on AWS, or GCP.
- Familiarity with DevOps practices (CI/CD, containerization, automated testing).
- Solid grasp of modern architecture patterns, including microservices and event-driven systems.
- Practical experience in streaming/event platforms like Kafka, Pub/Sub, Kinesis
- Practical experience deploying and serving models with vLLM, Ollama, or LM Studio.
- Applied experience with RAG and GraphRAG pipelines, including embedding models, chunking and retrieval strategies, and vector databases such as Pinecone, Weaviate, Milvus, Qdrant, Chroma, and pgvector.
- Familiarity with supporting orchestration and tooling such as LangChain, LlamaIndex, and knowledge-graph stores (e.g., Neo4j) for GraphRAG workloads.