Staff AI Engineer

Accountabilities:

• Shape the technical direction of AI products by translating product goals into clear architecture, priorities, technical decisions, and delivery plans.
• Lead architectural decisions across AI-assisted experiences, adaptive conversations, research synthesis, and other AI-powered capabilities.
• Own complex engineering initiatives from problem definition and experimentation through production deployment and ongoing operation.
• Design and build generative AI applications using large language models, RAG, vector search, tool use, and agentic systems.
• Develop reusable AI services and APIs while establishing scalable patterns for data flows, model orchestration, retrieval, and structured and unstructured data.
• Guide machine learning infrastructure using Python, Docker, Kubernetes, AWS, Terraform, CI/CD, and related cloud technologies.
• Build reliable batch and real-time processing pipelines using technologies such as Kafka and Airflow.
• Establish effective approaches for experimentation, model versioning, registries, deployments, and machine learning lifecycle management.
• Define evaluation strategies and release criteria covering accuracy, relevance, reliability, fairness, latency, security, and cost.
• Improve retrieval quality through techniques such as chunking, embeddings, context selection, and reranking, while connecting offline evaluation with production monitoring and customer feedback.
• Build safeguards for security, privacy, responsible AI, prompt injection, data leakage, and unexpected model behavior into system architecture.
• Establish engineering standards for testing, observability, incident response, deployment, security, and AI system operations.
• Mentor engineers and technical leads while helping teams make well-reasoned technical trade-offs without relying on formal authority.
• Evaluate emerging AI research, tools, and technologies and identify opportunities where they can deliver practical value.
• Partner with Product and Engineering teams to prioritize AI investments based on customer needs, technical feasibility, and measurable business impact.
• Communicate technical concepts, risks, dependencies, and trade-offs clearly to both technical and nontechnical stakeholders.

Requirements:

• Significant experience building and operating machine learning or AI systems in production, with demonstrated technical leadership beyond individual projects.
• Proven experience leading complex, cross-functional engineering initiatives from ambiguous requirements through measurable production outcomes.
• Strong Python and software engineering skills, with the ability to contribute directly to production systems.
• Experience designing production services and APIs using technologies such as FastAPI.
• Hands-on experience developing generative AI applications using large language models, RAG, tool use, agentic systems, or comparable approaches.
• Strong understanding of enterprise RAG architecture, including retrieval, chunking, embeddings, reranking, evaluation, and monitoring.
• Experience defining AI evaluation methodologies and using evidence to guide model, architecture, and release decisions.
• Experience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.
• Strong experience designing and operating cloud environments using AWS, Docker, Kubernetes, Terraform, and CI/CD practices.
• Familiarity with AWS services such as SageMaker or Bedrock.
• Experience with event-driven processing, vector databases, and machine learning lifecycle technologies such as Kafka and MLflow, or comparable tools.
• Experience establishing observability and diagnosing production issues using platforms such as Datadog or OpenSearch.
• Strong technical judgment and the ability to balance delivery speed, quality, reliability, scalability, security, and cost.
• Ability to influence technical decisions across teams, build alignment, and communicate complex concepts effectively.
• Experience mentoring engineers and improving the technical effectiveness of teams.
• Experience in a B2B SaaS environment, conversational AI, adaptive interviewing, automated analysis, or summarization is an advantage.
• Familiarity with text, audio, or video AI applications, shared AI platforms, Airflow, Argo Workflows, SQL, Spark, Snowflake, or other data technologies is a plus.
• Experience with AI security, privacy, responsible AI, prompt injection protection, data leakage prevention, or materially improving AI latency, reliability, or cost is also valued.

Benefits:

• Base salary range of $170,000–$210,000 USD.
• Additional 5–10% bonus depending on level and performance.
• Total compensation tailored based on location, experience, education, and skillset.
• Remote work environment.
• Opportunity to shape the technical direction of advanced AI products and scalable AI foundations.
• Cross-functional collaboration with Product, Engineering, Data Science, Data Engineering, and Analytics teams.
• Opportunity to influence AI architecture, evaluation, infrastructure, reliability, security, and customer-facing experiences.
• Inclusive, collaborative working environment focused on transparency, trust, and respect.