Applied AI Engineer

About the role

The Applied AI Engineer will focus on building and scaling production-ready AI systems that deliver meaningful business value. This role combines advanced AI development with strong software engineering practices, emphasizing reliability, scalability, and maintainability.

Responsibilities

• Design intelligent workflows, agent-based solutions, and AI-powered product experiences used by real customers.
• 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.
• Create retrieval-augmented generation (RAG) architectures, including data ingestion, embeddings, semantic search, and context management.
• Develop 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

• Experienced software engineer with strong AI expertise and a proven ability to build 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.
• Able 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.

Qualifications

• Authorization to work in the United States.

Preferred Qualifications

• 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

• Full-time 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.