Senior AI Automation Engineer

com ) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging.

About The Role

We are seeking a hands-on AI Automation Engineer to design, build, and optimize integrations, automations, and advanced AI workflows, including agentic architecture, that power our enterprise business operations. You’ll own the Workato automation stack, engineer scalable AI workflows on top of core platforms (Salesforce, NetSuite, Snowflake, Slack), and implement agentic capabilities that allow systems to act as intelligent tools, reducing manual work and accelerating insights. This role requires deep technical execution skills in integration development, agentic system design, and programming, combined with a practical understanding of AI workflows.

You will collaborate directly with stakeholders to translate business needs into scalable, automated solutions and deliver measurable improvements in system efficiency and operational performance. Your Area of Focus Integration Automation Development Design, build, and maintain Workato recipes, connectors, and orchestrations for Salesforce, NetSuite, Slack, and Snowflake. Implement error handling, observability, and reusable design patterns to ensure reliability and scalability.

Agentic System Design Architect multi-agent systems: tool selection, planning loops, state management, human-in-the-loop Checkpoints. Partner with stakeholders to design corporate systems (Salesforce, NetSuite, Snowflake, Slack, etc) as agent-callable tools, not just data sources. Build guardrails, fallbacks and error recovery for non-deterministic workflows.

Data/RAG Pipeline Design Management Design and automate data flow processes to support AI retrieval (RAG) and insights generation across core enterprise platforms. Build evaluation into every system to ensure high quality results, considering precision, answer faithfulness, latency, and cost. Own the Retrieval-Augmented Generation (RAG) lifecycle end-to-end, including ingestion, chunking strategy, embeddings, vector storage, and reranking.

Implement data quality checks and architectural safeguards to ensure trusted, high-fidelity datasets for AI agents. AI Operations Production observability: prompt/response tracing, cost monitoring, eval dashboards. Document agent behavior, decision logic and failure modes.

A/B testing agent versions, model comparisons. Programming for Automation Write modular, reusable scripts in Python, Ruby, SQL, or JavaScript to support integration, data transformation, and automation tasks. Governance Documentation Apply data governance practices for lineage, security, and retention.

Maintain technical documentation, diagrams, and version control via GitHub, Confluence, and Jira.

Qualifications

5+ years of experience in integration, automation, AI, or software engineering, with 1+ year hands-on in Workato. Proven expertise with Enterprise iPaaS required (Workato, Mulesoft, or similar). Deep proficiency in designing, implementing, and architecting complex AI workflows and integrations across core platforms (Salesforce, NetSuite, Slack, and Snowflake).

Hands-on technical background in programming (Python, Ruby, or similar), leveraging APIs to build and maintain scalable, high-fidelity enterprise systems. Experience using vector databases such as Pinecone, Snowflake Cortex Search, and pgvector for Retrieval-Augmented Generation (RAG). Experience with AI/ML concepts and implementing agentic architecture and AI workflows in enterprise environments.

Experience with Cloud Platforms such as GCP or AWS preferred. Experience with GitHub for version control, Jira for work tracking, and Confluence/Lucid for documentation.