Overview

Senior Software AIML Engineer Company Description It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis.

Responsibilities

• Design and build end-to-end full-stack applications, including frontend experiences, backend services, APIs, data layers, and cloud-native infrastructure.
• Build production-grade AI/ML-powered capabilities using LLMs, RAG, embeddings, semantic search, agentic workflows, and intelligent decision-making.
• Design and implement agentic architectures, including tool calling, orchestration, planning loops, memory, context management, and failure recovery.
• Develop reliable AI-powered APIs and services that integrate frontier models and enterprise data securely and efficiently.
• Build retrieval and grounding pipelines using vector search, hybrid search, semantic retrieval, re-ranking, and contextual enrichment. Establish evaluation and observability mechanisms to measure AI quality, accuracy, latency, cost, reliability, and safety.
• Take features from concept and prototype through production deployment and ongoing operation, with ownership of quality and reliability.
• Work with product, design, platform, data, security, and other engineering teams to translate ambiguous problems into scalable technical solutions. Contribute to architecture and design decisions, code reviews, engineering standards, and technical direction. Mentor engineers and help raise the bar on full-stack engineering and production AI development practices. What You'll Do
• Design and develop scalable, maintainable frontend applications and backend services using REST/GraphQL APIs, microservices, event-driven services, and distributed systems.
• Work with modern frontend technologies such as React, TypeScript, JavaScript, or equivalent frameworks.
• Build cloud-native applications with strong focus on scalability, performance, reliability, and security. Own software delivery across development, testing, deployment, monitoring, and production operations.
• Build applications leveraging LLMs, generative AI, embeddings, RAG, semantic search, and agentic workflows.
• Integrate frontier AI models and SDKs such as OpenAI, Anthropic, Google, or equivalent platforms. Apply prompt engineering, structured outputs, function/tool calling, context engineering, and model selection to real-world applications.
• Design agent workflows that can reason, use tools, retrieve information, execute actions, and recover from failures.
• Build AI evaluation frameworks and automated tests to measure model and application quality. Balance model capability, accuracy, latency, scalability, and cost when selecting and integrating AI models. Apply AI safety, security, privacy, governance, and guardrail practices to production AI systems.
• Explore and adopt emerging AI technologies and rapidly turn promising capabilities into production-ready solutions. AI-Native Engineering Practices Use AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, Windsurf, or equivalent tools as part of the software development lifecycle. Apply AI to improve engineering productivity across coding, testing, debugging, documentation, code review, and system design.
• Work Personas We approach our distributed world of work with flexibility and trust.