Staff Software Engineer
engradar
Job Description
- Design and implement AI-Native product development, using cutting-edge technical approaches to deliver business value
- Build high-quality, clean, scalable, and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit Testing, etc.)
- Drive complex, multi team technical initiatives and resolve architectural conflicts without management escalation
- Own your code from design through implementation, test automation, and delivery
- Collaborate with product managers to define feature sets that solve real customer problems
- Lead architecture and code reviews, mentor senior engineers, and raise the front end bar across the organization
- Build the evaluation infrastructure: golden datasets, multi-turn suites, judge calibration, CI gates, and drift detection.
- Bring judgment on where AI belongs in the engineering workflow, and set the guardrails for AI assisted development
- Make informed architectural and technical trade-off decisions between latency, cost, scalability, reliability, and model quality
- Stay current with emerging AI/ML frameworks, services, and techniques; evaluate and recommend adoption where applicable
- Contribute to code and architecture standards; ensure adherence to established best practices
- Experience in leveraging critical thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry.
- 8+ years of experience in software engineering, with a track record of delivering high-quality products.
- 3+ years of hands-on work with LLM prompting, agent design, and retrieval systems (RAG, semantic search,
- Experience integrating LLM APIs (Anthropic, OpenAI, Bedrock, Vertex, or similar), including tool/function calling, structured-output enforcement, and guardrails.
- Experience building and debugging evaluation harnesses and metrics for AI features, such as accuracy, reg detection.
- Working knowledge of embeddings, vector stores, and the failure modes of probabilistic systems, enough to debug and verify behavior in production.
- Strong JavaScript/Node.js and Python, Java, with solid data modeling and query optimization
- Strong knowledge of data structures, algorithms, object-oriented design, design patterns, and performance optimization
- Front-end experience with modern frameworks (React, Lit, Angular, or Vue) for conversation-first experiences.
- Automated testing (unit and integration), CI/CD, containers, and observability, plus on-call or incident experience with customer-facing systems.
- Current, effective use of AI coding agents, with evidence of accountable delivery: precise specs, critica harnesses.
- Demonstrated mentorship and a record of raising quality through code review.
- Clear communication with both engineers and non-technical stakeholders.
- Experience with LLM orchestration frameworks and vector databases (LangChain, LlamaIndex, pgvector, or similar).
- Familiarity with automated testing frameworks (e.g., JUnit, Playwright, TestNG) and integrating tests into CI/CD pipelines
- Understanding software quality principles including reliability, observability, and production readiness.
- Ability to troubleshoot complex systems and optimize performance across the stack.
- Experience with AI-powered tools or workflows, including validation of datasets, model predictions, and inference consistency.
- Comfort with development tools such as IDEs, debuggers, profilers, source control, and Unix-based systems