FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Tech Stack
Tools & technologiesAssemblyAWSCloudDistributed SystemsNoSQLPythonSQL
About the role
Key responsibilities & impact- Design, build, and maintain production-grade AI systems and customer-facing AI features
- Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services
- Build backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflows
- Design and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly
- Integrate foundation models through platforms such as Amazon Bedrock or Agent Core
- Develop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases
- Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes
- Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency
- Debug AI behavior through logs, traces, evaluations, user feedback, and production telemetry
- Collaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems
- Help establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability
- Mentor and collaborate with engineers across both software and AI disciplines
- Evaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability
Requirements
What you’ll need- US Citizen or authorized to work in US
- 5+ years of professional software engineering experience building production systems
- Strong proficiency in Python
- Strong backend engineering fundamentals and experience building scalable APIs, services, distributed systems, or workflow orchestration platforms
- Proven hands-on experience building and shipping AI-powered applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies in production environments
- Experience designing and implementing agentic workflows, tool-calling systems, structured outputs, prompt pipelines, or retrieval-augmented generation architectures
- Strong understanding of the practical challenges involved in production AI systems, including hallucination mitigation, evaluation, reliability, observability, latency, and cost management
- Experience building production software systems with strong engineering standards around testing, QA, deployment, monitoring, and maintainability
- Strong understanding of modern software engineering practices, including Git workflows, code review, CI/CD, automated testing, operational debugging, and release management
- Experience working with cloud infrastructure, preferably AWS
- Experience working with SQL and/or NoSQL databases
- Strong debugging, systems-thinking, and problem-solving skills
- Ability to operate effectively in fast-moving environments with evolving requirements and imperfect information
- Strong communication skills and ability to collaborate across technical and non-technical teams
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonAI systemsLLMsAPIsbackend servicesretrieval-augmented generationautomationcloud infrastructureSQLNoSQL
Soft Skills
problem-solvingcommunicationcollaborationmentoringsystems-thinkingdebuggingadaptabilityengineering standardsquality assuranceoperational reliability