Senior Applied AI Engineer
New
G
Givzey | Version2.aiApplied AI
Location: United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years of professional software engineering experience
- Required Skills
- AWSPythonRESTful APIsDistributed Systems
Requirements
- Have 7+ years of professional software engineering experience, including substantial experience designing and delivering production systems.
- Demonstrate strong proficiency in Python and modern backend software engineering.
- Have technical leadership experience for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
- Have hands-on experience designing, building, and shipping production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies.
- Have strong knowledge of backend architecture, APIs, distributed systems, databases, and workflow orchestration.
- Have experience with agentic workflows, tool-calling systems, structured outputs, or retrieval-augmented generation architectures.
- Understand production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.
- Demonstrate software engineering best practices, including automated testing, code review, CI/CD, system design, and maintainability.
- Have experience working with cloud infrastructure; AWS is preferred.
- Be able to mentor engineers, provide constructive technical feedback, and influence engineering practices.
- Balance hands-on engineering contributions with technical leadership responsibilities.
- Preferred: experience with Amazon Bedrock, AWS AgentCore, AWS services, agent orchestration frameworks, vector databases, semantic retrieval, or LLM observability and evaluation platforms.
Responsibilities
- Lead small engineering teams in designing, developing, and delivering complex AI initiatives.
- Own project execution from initial design through implementation, testing, deployment, and ongoing operation.
- Translate ambiguous product requirements into technical approaches, engineering tasks, and delivery milestones.
- Drive architectural discussions, evaluate tradeoffs, and establish implementation strategies.
- Design and maintain production AI systems using LLMs, agentic workflows, retrieval architectures, and backend services.
- Build agent workflows with tool calling, structured outputs, multi-step reasoning, validation, and human-in-the-loop controls.
- Build and optimize retrieval-augmented generation systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly.
- Develop evaluation frameworks, regression testing, golden datasets, trace analysis, and quality measurement for AI workflows.
- Improve AI systems for reliability, accuracy, latency, observability, and cost efficiency.
- Mentor engineers and improve engineering standards through code reviews, architectural guidance, and hands-on collaboration.
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