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 AI-driven workflows that power our enterprise data and business operations. You’ll own the Workato automation stack, develop resilient data pipelines between core platforms (Salesforce, NetSuite, Snowflake, Slack), and implement AI-enabled capabilities that reduce manual work and accelerate insights. This role requires deep technical execution skills in integration development, data modeling, and programming, combined with a practical understanding of AI workflows.

You will collaborate directly with stakeholders to translate business needs into scalable, automated solutions — delivering measurable improvements in data accessibility, 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 Develop and automate ELT/ETL processes to support both BI Analytics and AI retrieval across similar datasets with different access patterns. Build evaluation into every pipeline to ensure high quality results considering precision, answer faithfulness, latency and cost. Own retrieval end-to-end including ingestion, chunking strategy, embeddings, vector storage and reranking as one connected system.

Implement data enrichment, quality checks, and architectural safeguards to maintain trusted datasets. 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

2-5 years of experience in integration, automation, AI, software or data engineering, with 1+ year hands-on in Workato. Proven expertise with Enterprise iPaaS required (Workato, Mulesoft, or similar). Proficiency in designing and implementing integrations across Salesforce, NetSuite, Slack, and Snowflake.

Hands-on technical background in ETL/ELT, Reverse-ETL, data modeling, SQL/T-SQL, and programming (Python, Ruby, or similar). Experience using vector databases such as Pinecone, Snowflake Cortex Search and pgvector. Experience with AI/ML concepts and implementing AI workflows in enterprise environments.

Hands-on experience leveraging APIs and programming languages to build and maintain scalable enterprise integrations. Experience with Cloud Platforms such as GCP or AWS preferred. Hands-on experience using APIs and programming languages to build and maintain scalable enterprise integrations.

Experience with GitHub for version control, Jira for work tracking, and Confluence/Lucid for documentation.