Machine Learning Engineer, CX Intelligence
Build and scale production multi-agent systems for customer support, integrating LLMs with internal tools and APIs. The role requires 3+ years of production ML/AI experience, strong Python and microservices expertise, and familiarity with orchestration, RAG, evaluation, and vector databases.
About the job
Responsibilities
- Architect multi-agent systems using orchestration frameworks such as LangGraph and Google ADK to automate complex customer support procedures end to end.
- Build and scale Model Context Protocol (MCP) integrations connecting LLMs with internal APIs, databases, and third-party tools.
- Develop automated LLM-as-a-judge evaluation pipelines to monitor, measure, and improve non-deterministic AI agents in production.
- Implement retrieval-augmented generation (RAG), fine-tuning, and prompt engineering to ensure chatbot responses are grounded, accurate, and compliant.
- Ship production-ready Python services that are resilient, low-latency, and capable of handling high-scale traffic across asynchronous microservices.
- Partner with Conversation Design and Product to translate complex business logic into executable agent procedures.
Requirements
- 3+ years building and scaling machine learning or artificial intelligence systems in production.
- Experience implementing agentic Loop- or ReAct-based systems where AI takes autonomous actions.
- Understanding of the LLM lifecycle, including context-window management, token optimization, structured-output parsing, Pydantic, JSON mode, and multi-agent orchestration.
- Strong Python proficiency and hands-on experience with microservices architecture, asynchronous programming, high-throughput APIs, and vector databases such as Pinecone or Weaviate.
- Ability to explain model behavior and technical trade-offs to non-technical stakeholders across CX, Legal, and Product.
- Responsible use of generative AI with human oversight and focus on measurable improvements in workflow efficiency, cost, and quality.
Compensation and Benefits
- Annual base salary: R$347,900–R$347,900 BRL.
- Total compensation may include equity, bonus eligibility, and medical, dental, and vision benefits.
Skills
Python, LangGraph, Google Adk, Model Context Protocol, LLMs, RAG, Prompt Engineering, Fine-Tuning, Pydantic, JSON, Microservices, Asynchronous Programming, Vector Databases, Pinecone, Weaviate
Similar jobs
ML Engineering jobs
Rollstack
United StatesBuild production AI capabilities for automated slide and document generation, working across LLM applications, data analysis, and content generation. The role requires 3+ years in machine learning and NLP, advanced Python, and experience with LLM frameworks and production systems.
Protege
Remote
Build the technical foundation for a new business vertical, creating reusable infrastructure and leading early customer engagements from scoping through delivery. The role requires 3+ years of engineering experience, strong Python and SQL skills, backend/data expertise, and comfort operating in ambiguity.
Adaption Labs
San Francisco, CA
Build production agent systems that plan, use tools, recover from failures, and improve over time. The role requires 5+ years of production ML or backend experience, LLM or agent deployment experience, and expertise in evaluation, tracing, observability, and agent architecture.
Adaption Labs
San Francisco, CA
Own inference-stack cost and performance by optimizing serving, caching, batching, quantization, decoding, routing, and GPU execution. The role requires 5+ years in ML systems, inference infrastructure, or performance engineering, plus strong Python and systems-language skills.
Kinter
Brazil
Build and operate production AI agents for finance workflows, owning orchestration, evaluation, reliability, and human-in-the-loop safeguards. Requires 8+ years building SaaS products, 2+ years shipping agentic systems, and hands-on Node.js and TypeScript experience.