Lead Staff AI Engineer

Lead Staff AI Engineer
Remote
Direct Hire
Salary Target: $200k – $220k

This growing SaaS technology company builds enterprise software used by organizations to automate workflows, streamline operations, and deliver digital experiences to customers. As part of a new AI initiative, the company is investing heavily in building AI-driven tools and platforms that integrate directly into customer workflows and applications.

Job Description:
Our client is seeking a Staff AI Engineer to help lead the development of AI-powered systems and agent-based platforms that power conversational and automated experiences for enterprise users. This person will act as a technical leader on a growing engineering team while remaining highly hands-on with architecture and development.
The team is focused on building AI tools that are delivered directly to users, so the ideal candidate has experience developing production AI systems and understands how people interact with AI-driven applications. This role will help guide technical direction while working closely with product and engineering teams to bring new AI capabilities to market.

Required Skills:
• 6+ years of AI/ML engineering experience
• Strong experience building production AI or ML systems
• Experience working with LLMs, agent-based systems, or AI-driven applications
• Experience developing RAG pipelines, embeddings, prompt engineering, or model evaluation workflows
• Familiarity with vector databases such as Pinecone, Weaviate, FAISS, or Milvus
• Experience with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar tools
• Experience designing scalable APIs and distributed systems
• Strong backend engineering experience, Python preferred
• Experience deploying AI systems in cloud environments such as AWS, Azure, or GCP
• Experience mentoring engineers or acting as a technical lead on engineering teams


Bonus / Soft Skills:
• Experience building agentic AI platforms or conversational AI systems
• Experience building AI products used directly by customers
• Experience with MLOps, observability, or evaluation tools such as MLflow, LangSmith, Weights & Biases, or similar platforms
• Experience with containerization and cloud-native tools such as Docker or Kubernetes
• Experience with machine learning frameworks such as PyTorch or TensorFlow


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