Senior Applied AI Engineer
New
J
JobgetherApplied AI
Based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 7 years of professional software engineering experience
- Required Skills
- AWSPythonCI/CDDistributed Systems
Requirements
- At least 7 years of professional software engineering experience, including substantial responsibility for designing and delivering production systems.
- Strong proficiency in Python and modern backend software engineering.
- Experience providing technical leadership for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
- Hands-on experience designing, building, and deploying production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies.
- Strong understanding of backend architecture, APIs, distributed systems, databases, and workflow orchestration.
- Experience designing agentic workflows, tool-calling systems, structured outputs, or retrieval-augmented generation architectures.
- Knowledge of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.
- Experience with automated testing, code reviews, CI/CD, system design, and maintainability.
- Experience working with cloud infrastructure, preferably AWS.
- Ability to mentor engineers and balance hands-on engineering contributions with technical leadership responsibilities.
- Preferred experience with Amazon Bedrock, AWS AgentCore, AWS services, agent orchestration frameworks, vector databases, or LLM observability and evaluation platforms.
Responsibilities
- Lead small engineering teams in designing, developing, and delivering complex AI initiatives.
- Own technical execution from architecture and implementation through testing, deployment, and ongoing operations.
- Translate ambiguous product requirements into technical strategies, engineering tasks, and delivery milestones.
- Design, build, and maintain production AI applications using LLMs, agentic workflows, retrieval architectures, and backend services.
- Develop agent workflows with tool calling, structured outputs, multi-step reasoning, validation, and human-in-the-loop controls.
- Build and optimize RAG systems, including ingestion pipelines, embeddings, semantic search, and context assembly.
- Develop AI evaluation frameworks and improve production systems across reliability, accuracy, latency, observability, and cost.
- Provide code reviews, architectural guidance, and hands-on mentorship to engineers.
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