Senior/Staff Software Engineer, AI/ML

Recruiting from Scratch is a premier talent firm that focuses on placing top talent at innovative companies. They are seeking a Senior/Staff Software Engineer specializing in AI/ML to design and deploy AI-powered applications and infrastructure, while collaborating with various teams to enhance healthcare products.

Responsibilities:

• Design, build, and deploy end-to-end AI-powered applications and infrastructure
• Build production-grade LLM systems using RAG, agent frameworks, orchestration layers, and modern AI tooling
• Develop AI-powered chatbots, recommendation engines, search systems, and intelligent workflows
• Own AI/ML product development from 0-to-1, from architecture through production deployment
• Design scalable ML pipelines for experimentation, fine-tuning, evaluation, and deployment
• Work closely with product, data, analytics, and engineering teams to deliver AI-native product experiences
• Build backend systems and APIs supporting AI workloads and inference pipelines
• Drive technical architecture and engineering best practices across AI systems
• Work with modern AI platforms including OpenAI APIs, Vertex AI, PyTorch, TensorFlow, and agent frameworks
• Translate healthcare and user data into intelligent, production-ready AI systems
• Contribute to AI platform strategy and help shape the company’s long-term AI roadmap
• Operate autonomously in a fast-moving, highly ambiguous AI product environment
• Balance rapid iteration with scalability, safety, reliability, and performance
• Collaborate cross-functionally with product and business stakeholders to identify impactful AI opportunities
• Stay current with emerging LLM, RAG, and agentic AI technologies and integrate them into production systems

Requirements:

• 6–15 years of experience in software engineering or AI/ML engineering roles
• Strong software engineering foundation with backend-heavy product engineering experience
• Experience building AI/ML products end-to-end in production environments
• Hands-on experience with LLMs, RAG systems, and agent frameworks
• Experience shipping production AI products from 0-to-1
• Strong Python engineering experience
• Experience with chatbot systems, recommendation engines, search, or GenAI applications
• Experience working across the full ML lifecycle including ingestion, preprocessing, training, deployment, and monitoring
• Strong distributed systems and backend architecture skills
• Experience collaborating closely with product and cross-functional teams
• Comfortable operating autonomously in fast-moving startup or high-growth environments
• Strong builder mentality with high ownership and execution ability
• Comfortable writing production-grade code without heavy AI tooling dependence
• Ability to explain AI systems and technical tradeoffs clearly to non-technical stakeholders
• Experience building production LLM applications at scale
• Experience with RAG architectures and retrieval pipelines in production
• Experience with AI agent frameworks and orchestration systems
• Experience building chatbot, recommendation, or intelligent search systems
• Strong backend engineering foundation transitioning into AI/ML
• Experience with OpenAI APIs, Vertex AI, LangChain, LlamaIndex, or similar tooling
• Healthcare or digital health AI experience
• Experience handling high-volume production AI workloads
• Experience measuring production AI system performance and reliability
• Strong product-minded engineering instincts
• Experience working at high-growth product companies
• Experience owning projects from architecture through deployment
• Experience balancing ML experimentation with production engineering rigor
• Strong understanding of distributed systems and scalable infrastructure
• Experience working with PyTorch or TensorFlow
• Strong coding interview and system design capability
• Ability to discuss production metrics, scale, and operational performance of AI systems
• Experience building AI features with meaningful user or business impact
• Experience working on AI-powered user-facing products instead of purely internal ML infrastructure