AI Productivity Analyst
Build and own production AI systems, including agentic workflows, RAG applications, evaluation pipelines, and LLM-powered services. The role requires 5+ years of relevant engineering experience, strong Python skills, and expertise deploying reliable AI applications.
About the job
Responsibilities
- Design, build, and maintain production-grade AI systems, including multi-agent workflows, RAG applications, workflow automation, and LLM-powered services.
- Lead technical implementation of complex AI initiatives from discovery through deployment.
- Architect scalable backend services, APIs, evaluation pipelines, and retrieval systems.
- Improve reliability, observability, and performance of production AI applications.
- Define engineering standards, reusable components, and best practices for AI development.
- Collaborate with business stakeholders to understand problems and translate them into scalable AI solutions.
- Evaluate foundation models, frameworks, and prompting strategies and recommend adoption.
- Build evaluation frameworks and automated testing strategies for LLM-based systems.
- Mentor junior engineers through code reviews, technical guidance, and design discussions.
- Participate in architectural planning, sprint planning, technical reviews, and cross-functional initiatives.
- Drive improvements in engineering processes, documentation, and operational excellence.
Requirements
- 5+ years of experience in software engineering, machine learning engineering, AI engineering, or a similar technical role.
- Strong Python development skills and experience building and maintaining production systems.
- Deep understanding of LLMs, prompting techniques, structured outputs, tool calling, function calling, and AI application architectures.
- Experience designing and deploying RAG systems, including chunking strategies, embeddings, retrieval optimization, reranking, and evaluation.
- Experience building agentic workflows with frameworks such as LangGraph, LangChain, OpenAI Agents SDK, or similar.
- Experience integrating external APIs and enterprise systems.
- Strong understanding of software engineering practices, including testing, CI/CD, observability, monitoring, and production deployments.
- Experience with cloud platforms, preferably Google Cloud, and containerized applications.
- Strong debugging and problem-solving skills.
- Excellent written and verbal English communication.
- Ability to independently manage projects and influence technical decisions across teams.
Preferred Qualifications
- Bachelor's or master's degree in Computer Science, Engineering, AI, or a related field.
- Experience deploying AI systems into production at scale.
- Experience with vector databases such as Vertex AI Vector Search or pgvector.
- Experience building evaluation frameworks for LLM quality, latency, and cost optimization.
- Experience with orchestration platforms such as n8n, LangGraph, Airflow, or similar.
- SQL and BigQuery experience.
- Contributions to open-source AI projects or notable personal AI applications.
Compensation and Benefits
- Competitive salary.
- Comprehensive benefits and perks.
- Opportunities for growth.
- Robust training program.
- Access to AI tools that support employee productivity.
Skills
Python, LLMs, Prompt Engineering, RAG, LangGraph, LangChain, Openai Agents Sdk, APIs, CI/CD, Observability, GCP, Docker, Vector Databases, SQL, BigQuery
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