Applied AI Engineer
New
Based in United StatesFull-TimeSenior
Salary150,000 - 200,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLNosqlCI/CDLLMGenerative AIDistributed Systems
Requirements
- 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.
Responsibilities
- 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.
- Establish strong software engineering practices around testing, deployment, CI/CD, code reviews, and maintainable AI development workflows.
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