Develop and deploy complex AI workflows (advanced RAG, multi-agent systems) integrated with core productivity tools.
Build robust APIs and backends for RAG Pipelines and AI agents.
Design and maintain scalable data pipelines for ETL, ingestion, and data preparation to ground models in high-quality data.
Manage the testing deployment lifecycle, automating and versioning updates.
Orchestrate models on Vertex AI (fine-tuning, serving, monitoring).
Implement AIOps and Agent Observability (tracing, logging, monitoring), while enforcing security for model inputs, outputs, and infrastructure.

Requirements

6+ years of professional software/data engineering experience, including at least 2 years of production-level hands-on experience in AI development/engineering.
Proven experience with advanced RAG use cases, at least one major GenAI framework (e.g., LangChain, LlamaIndex, or Heystack) plus one multi-agent framework (e.g., Langgraph, Google ADK, Vertex AI Agent Builder, or Microsoft Agent Framework).
Expert-level Python (or Typescript) skills. You write clean, modular, production-ready code.
You are proficient with Pydantic (or an equivalent in Typescript) for validation and FastAPI for high-performance Async I/O.
Essential expertise in Google Cloud, BigQuery (complex SQL, data warehousing) and Vertex AI (deployment, serving).
Strong capability in building ETL/ELT pipelines.
You are comfortable with Git , Docker , and Kubernetes.
You are experienced in devops and know how to set up CI/CD pipelines to automate all the necessary testing and deployment.
Practical knowledge of securing applications (API security, secrets management, IAM, input sanitization).
Familiarity with MCP and A2A protocols.
Hands-on experience with some LLMOps and AI evaluation framework (e.g. GenAI Evals on Vertex, DSPy, RAGAs) as well as high quality vector databases (e.g. Weaviate, Milvus, Qdrant, or Neo4j).

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI developmentRAG workflowsAPI developmentdata pipelinesETLPythonFastAPIGoogle CloudBigQueryCI/CD