Why this role
Chargebee’s GBT AI team builds internal AI agents and workflows that power smarter, faster operations across Finance, HR, Legal , Marketing, RevOps and GTM.
We’re looking for an AI Engineer who can think end-to-end—understand the business process, design the right solution (not just an out-of-the-box tool), and ship robust agentic systems that deliver measurable impact.
What you’ll do
- Design, build, and ship AI agents that automate internal workflows (e.g., case triage, knowledge assistants, data reconciliation, summarization, entity extraction, task orchestration).
- Own the full lifecycle: problem framing, data/knowledge mapping, prototype → production, instrumentation, and iteration based on metrics.
- Integrate with the GBT Tech stack using APIs, webhooks, and internal services across our SaaS stack (ticketing/CRM/data warehouse).
- Engineer LLM prompts & tools (function calling, tools/toolkits, retrieval/RAG, multi-step planning) and select the right model for the job.
- Evaluate and harden solutions with offline tests, golden sets, guardrails, and observability; drive down hallucinations and failure modes.
- Document clearly and partner with stakeholders to align on success criteria, SLAs, and ongoing maintenance.
- (Nice-to-have ML): apply ML where it adds value—basic classifiers/embeddings, light fine-tuning/adapters, feature work, and A/B evaluation.
What you’ll bring (must-haves)
2-6 years of professional software experience, including hands-on delivery of at least one AI agent or workflow in production or a serious pilot.
- Practical LLM API experience: you’ve integrated OpenAI and/or Anthropic (Claude) (or comparable model APIs) to build real features.
- Strong coding in Python or TypeScript, with sound software engineering practices (testing, code reviews, CI/CD, Git Best Practices).
- Working knowledge of prompt design, function/tool calling, RAG (vector stores, chunking, indexing), and pipeline orchestration.
- Comfort with HTTP APIs, authentication, and integrating multiple systems into a coherent solution.
- Experience in developing, debugging and optimizing data pipelines and transformations using Python/Pandas/SQL.
- Experience working with at least one no-code or low-code agentic workflow automation tool such as n8n, Zapier, or OpenAI Agent Builder.
Nice to have
- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar agent frameworks.
- Observability/eval tools (e.g., LangSmith, Phoenix/Arize, Weights & Biases, OpenTelemetry).
- Vector databases (Pinecone, Weaviate, pgvector,etc) and data systems (SQL, dbt, warehouse basics).
- Knowledge of ML fundamentals (classification,forecasting, embeddings, evaluation)
- Security & compliance awareness (PII handling, access controls, red-team/guardrails).
- SaaS/B2B domain familiarity; subscription billing/revenue ops context is a plus.
- Experience working with major cloud technologies (AWS, Azure, or GCP)
- Experience integrations data to GTM Tech stack and other business systems such as SFDC, Netsuite, Success Factors, ADP, Google Big Query
How we work
- Small, outcome-oriented team that ships iteratively with clear success metrics (accuracy, deflection rate, cycle time, $ impact).
- Bias to automation + ownership: you’ll take features from discovery → design → deployment.
- Model-agnostic approach: choose the right tool/model for quality, latency, and cost.
Apply with
- A short note on an agent or AI workflow you’ve built, your role, tech stack, and impact.
Links (repo, demo, doc) welcome.