Accountabilities:

The Applied AI Engineer will be responsible for designing, developing, and improving production-grade AI systems while ensuring they are reliable, scalable, and aligned with business needs. The role requires strong ownership of AI workflows, backend infrastructure, and engineering practices that enable continuous innovation.

• Build and maintain production AI applications, including agentic workflows, AI-powered product features, and automation systems.
• Develop AI solutions using large language models, retrieval systems, APIs, backend services, and workflow orchestration frameworks.
• Design and implement retrieval-augmented generation (RAG) architectures, including data ingestion, embeddings, semantic search, and context management.
• Create backend services and infrastructure that allow AI systems to securely interact with business workflows and data sources.
• Develop evaluation frameworks, testing processes, monitoring systems, and observability solutions to improve AI quality and reliability.
• Implement prompting strategies, structured outputs, guardrails, and workflow logic for real-world AI applications.
• Monitor and optimize AI systems for performance, latency, cost efficiency, and operational stability.
• Debug and improve AI behavior using production telemetry, logs, evaluations, user feedback, and system traces.
• Collaborate with engineering, product, operations, and customer teams to translate complex requirements into scalable solutions.
• Establish strong software engineering practices around testing, deployment, CI/CD, code reviews, and maintainable AI development workflows.
• Share knowledge, mentor peers, and contribute to improving AI engineering standards across the organization.
• Evaluate emerging AI technologies and determine practical applications based on measurable impact and long-term sustainability.

Requirements:

The ideal candidate is an experienced software engineer with strong AI expertise and a proven ability to build production systems. They should combine technical depth in modern AI technologies with strong engineering judgment, problem-solving skills, and the ability to operate effectively in an evolving environment.

• 5+ years of professional software engineering experience building and maintaining production systems.
• Strong proficiency in Python and experience developing scalable backend applications.
• Strong understanding of backend engineering fundamentals, including APIs, distributed systems, workflow orchestration, and system design.
• Hands-on experience building and deploying AI-powered applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies.
• Experience designing agentic workflows, tool integrations, structured outputs, prompt pipelines, or RAG-based architectures.
• Strong knowledge of production AI challenges, including hallucination prevention, evaluation, observability, reliability, latency, and cost management.
• Experience with modern software engineering practices, including Git workflows, automated testing, CI/CD, monitoring, debugging, and release management.
• Experience working with cloud infrastructure, preferably AWS.
• Experience with SQL and/or NoSQL databases.
• Strong analytical thinking, debugging skills, and ability to solve complex technical challenges.
• Ability to work independently, manage ambiguity, and deliver results in a fast-paced environment.
• Strong communication skills with the ability to collaborate effectively with both technical and non-technical stakeholders.
• Authorization to work in the United States.

Preferred qualifications include:

• Experience with AWS services such as Amazon Bedrock, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, or EventBridge.
• Experience with AI orchestration frameworks such as LangGraph, LangChain, DSPy, Semantic Kernel, or similar tools.
• Experience building multi-step AI agents that interact with tools, APIs, and external systems.
• Experience implementing AI evaluation systems, prompt regression testing, trace analysis, and human-in-the-loop workflows.
• Familiarity with vector databases and semantic retrieval technologies such as OpenSearch, pgvector, Pinecone, Weaviate, or FAISS.
• Experience with LLM observability and AI monitoring platforms.
• Experience working in startup environments or high-ownership product teams.
• Experience mentoring engineers and contributing to engineering culture improvements.

Benefits:

• Fully remote work environment within the United States.
• Opportunity to build innovative AI systems with direct customer impact.
• High ownership role with the ability to influence AI engineering practices and product direction.
• Collaboration with multidisciplinary teams across engineering, product, and operations.
• Opportunity to work with modern AI technologies, including LLMs, agents, retrieval systems, and automation frameworks.
• Supportive environment focused on engineering excellence, continuous improvement, and professional growth.
• Competitive compensation package ranging from $150,000 to $200,000 annually.
• Opportunity to contribute to meaningful AI solutions designed to solve real-world business challenges.