Lead Software Engineer

RSM•Irvine, CA

About The Position

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.

Requirements

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.
Excellent leadership, communication, and stakeholder management skills.
Strong problem-solving and decision-making abilities.
Ability to model complex business domains using entity resolution, expected-versus-actual reconciliation, hierarchy/graph structures, membership over time, and exception-driven workflows.
Strong understanding of distributed workflow design, including partial completion, re-processing, re-receipt/versioning, idempotency, and preserving user decisions across changing source data.
Demonstrated ability to reason from first principles, identify system boundaries, prevent logic leakage across layers, and design for extensibility without creating parallel code paths.
Experience building configuration-driven platforms where business rules, rates, thresholds, mappings, and policies are treated as governed data rather than hard-coded logic.
Familiarity with effective dating, version pinning, approval workflows, append-only audit trails, and replayable execution models.
Ability to design deterministic execution pipelines, including dependency graphs, DAG orchestration, bounded concurrency, failure isolation, caching, and recomputation strategies.
Experience separating calculation engines from user experience layers to avoid duplicated business logic and inconsistent results.
Strong architectural judgment with the ability to distinguish what belongs in shared platform services versus application-specific logic.
Bachelor’s or master’s degree in computer science, software engineering, or a related field.

Responsibilities

Lead the design, development, and delivery of secure, scalable, cloud-native software platforms that support complex business workflows, data-intensive applications, and AI-enabled capabilities.
Provide technical leadership across architecture, engineering design, code quality, testing strategy, deployment practices, and operational readiness for high-impact solutions.
Design and guide implementation of distributed systems, APIs, microservices, event-driven workflows, data pipelines, and integration patterns that are reliable, observable, and maintainable.
Drive architecture decisions that separate shared platform services, application-specific logic, calculation engines, and user experience layers to reduce duplication and improve system consistency.
Partner with product owners, architects, data teams, AI/ML engineers, security, and business stakeholders to translate ambiguous requirements into practical technical designs and delivery plans.
Build and evolve systems that preserve provenance, lineage, auditability, version history, and traceability from source data through transformed, computed, and user-facing outputs.
Develop and support configuration-driven platforms where rules, mappings, thresholds, policies, and business logic can be governed, versioned, approved, and executed without unnecessary code changes.
Guide engineering teams in implementing deterministic execution patterns, including dependency graphs, workflow orchestration, idempotency, replayable processing, caching, failure isolation, and recomputation strategies.
Support AI-enabled solution design, including RAG, GraphRAG, vector retrieval, knowledge graphs, model-serving patterns, model evaluation, and guardrails that ensure outputs are explainable, grounded, and reviewable.
Review and validate technical deliverables, architecture artifacts, pull requests, data models, integration designs, and operational controls to ensure quality, security, scalability, and compliance with engineering standards.
Coach and mentor engineers by raising the technical bar, promoting first-principles problem solving, encouraging thoughtful tradeoffs, and strengthening team capability in modern software engineering practices.
Continuously improve engineering practices, CI/CD pipelines, automation, observability, testing coverage, documentation, and incident response processes.
Serve as an escalation point for complex technical decisions, production issues, architectural tradeoffs, and cross-functional dependencies.
Contribute to technology strategy, platform modernization, departmental planning, and cross-functional initiatives that support reusable, governed, and extensible enterprise systems.