Note: The job is a remote job and is open to candidates in USA. The client is building an AI-native virtual healthcare clinic serving women and families across fertility, maternity, pediatrics, menopause, and family healthcare. The Senior/Staff Software Engineer will design, build, and deploy production AI applications and infrastructure, including LLM systems, chatbots, recommendation engines, search systems, and intelligent workflows. The role also involves owning end-to-end AI/ML product development, scalable ML pipelines, backend systems, and long-term AI platform strategy.
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
Skills
- 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
Benefits
- Competitive equity package
- Opportunity to help build foundational AI infrastructure at massive scale
- Work directly on cutting-edge AI systems impacting millions of healthcare users
- Exposure to advanced LLM, RAG, and agentic AI deployment challenges
- Strong technical ownership and autonomy
Company Overview