Ai / ml engineer
7 days ago
Why this roleChargebee’s GBT AI team builds internal AI agents and workflows that power smarter, faster operations across Finance, HR, Legal , Marketing, Rev Ops 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 doDesign, 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 Saa S 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 Open AI and/or Anthropic (Claude) (or comparable model APIs) to build real features.Strong coding in Python or Type Script, 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 Open AI Agent Builder.Nice to haveExperience with Lang Chain, Lang Graph, Llama Index, Semantic Kernel, or similar agent frameworks.Observability/eval tools (e.g., Lang Smith, Phoenix/Arize, Weights & Biases, Open Telemetry).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).Saa S/B2 B 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 QueryHow we workSmall, 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 withA short note on an agent or AI workflow you’ve built, your role, tech stack, and impact. Links (repo, demo, doc) welcome.
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