AI Engineer

We're hiring an AI engineer to build and ship the agentic systems at the core of RevGain's product, the Agentic Query Engine, AutoCSM workflows, and the services that surround them. This is an engineering role first: you write production code every day, own what you ship end to end, and are accountable for its reliability in front of enterprise customers. You should be a strong software engineer who has already taken LLM-based agentic systems from design through deployment and operation, not someone who has only prototyped in notebooks or consumed pre-built APIs.

Responsibilities:

• Design, build, and ship LLM-based agentic workflows in production multi-step tool calling, agent orchestration (e. g., Google ADK, CrewAI, LangGraph, or custom), and RAG pipelines.
• Own your services end to end: API design, data integration, deployment, monitoring, and rollback.
• Build evaluation harnesses and guardrails for agents, define quality metrics, systematically diagnose failure modes, and optimize for cost and latency.
• Integrate third-party tooling (LLM providers, vector databases, orchestration frameworks) while preserving RevGain's vendor-neutral architecture.
• Work closely with product and data engineering to turn requirements into shipped features that move real revenue-operation outcomes.
• Uphold engineering standards across the AI codebase, code review, testing, observability, and production troubleshooting.

Requirements:

• 5+ years building production software, with strong Python and solid engineering fundamentals, testing, code review, API design, and debugging live systems.
• Hands-on experience developing and deploying agentic AI systems in production: tool calling, orchestration frameworks or custom orchestration, RAG, and prompt engineering.
• A working evaluation discipline: You have built eval harnesses for LLM-based systems and can show how you diagnosed and fixed failure modes.
• Comfort with cloud infrastructure (AWS/GCP/Azure), containers (Docker/Kubernetes), and CI/CD.
• Experience working with structured and unstructured enterprise data (CRM, product usage, support/ticketing data).
• Clear communication: able to explain technical trade-offs to product and business stakeholders.
• B. Tech/M. Tech/M. S. in Computer Science or a related field, or equivalent practical experience.

Good to Have:

• Grounding in classical ML and applied statistics (forecasting, classification, churn/anomaly detection).
• Fine-tuning experience and familiarity with MLOps tooling (model serving, experiment tracking, feature stores).
• Production experience with more than one agent framework and a point of view on when to use each.
• Exposure to RevOps, CRM, or customer-success platforms and their data models.
• Open-source contributions or published technical work.