AI / ML Engineer

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.