Senior Data Scientist – Generative AI, Agents

Leega

. Develop and version agents’ system prompts, including guardrails, permitted actions, flow control, tool-calling policies, output format, and human escalation.

Posted 10/7/2026contractRemote • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and evaluating AI agents, with a strong focus on Generative AI, LLMs, and RAG strategies. Proficient in data processing, document information extraction, and compliance with AI governance standards.

Highest-signal resume keywords

Generative AI ProjectsLLM Prompt EngineeringRAG Strategy EvaluationPython Data ProcessingAI Governance Compliance

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ScienceMachine LearningDocument Information ExtractionEvaluation MetricsAnonymization TechniquesOCR WorkflowsEmbedding TechniquesHybrid SearchRegression TestingToken Cost Optimization

Soft Skills

CollaborationCommunicationProblem-Solving

Tools & Technologies

Azure OpenAIMongoDB Atlas Vector SearchMicrosoft TeamsServiceNowRPA Automations

Certifications & Qualifications

Azure AI Engineer

Industry Keywords

HealthcarePharmaceutical RetailAI GovernanceBrazil's LGPDRegulatory Affairs

Tech Stack

Tools & technologies

AzureCloudJavaScriptMongoDBNode.jsPythonReactRPAServiceNowTypeScript

About the role

Key responsibilities & impact

Develop and version agents’ system prompts, including guardrails, permitted actions, flow control, tool-calling policies, output format, and human escalation.
Prepare and structure knowledge bases for RAG, including cleaning, sectioning, validity metadata, and organization by categories and access groups.
Evaluate retrieval quality and fine-tune the RAG strategy.
Build OCR and information-extraction workflows for PDFs, photos, and handwritten documents, with quality metrics and handling for illegible documents.
Implement anonymization and personal-data masking services, along with input and output guardrails.
Build golden sets and a prompt evaluation harness, with regression testing for each version and measurement of accuracy and cost per case.
Evaluate and approve LLMs from different providers, applying AI FinOps practices.
Create agents on the client’s platform with the technical team and support integration testing, UAT, and assisted operations.
Document technical decisions and support materials for the architecture committee, covering costs, security, and data handling.
Work on a project to build five AI agents to automate back-office processes for a healthcare and pharmaceutical retail client.
Integrate agents with enterprise systems through APIs, MCPs, and RPA automations, with interaction via Microsoft Teams.
Work in a squad with a solutions architect, developer, and business analyst.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Information Systems, or a related field.
4+ years of experience in data science or machine learning, including experience on Generative AI projects.
Hands-on experience with LLMs: prompt engineering, function calling/tools, structured output (JSON Schema), and agents.
Experience with RAG: embeddings, vector databases, hybrid search, reranking, and retrieval evaluation.
Python for experimentation, evaluation, and data processing.
Building datasets and evaluation metrics for LLM solutions (accuracy, precision/recall, LLM-as-a-judge).
Knowledge of OCR and document information extraction (multimodal models and/or Document AI).
Knowledge of AI governance and Brazil’s LGPD: masking, anonymization, and data minimization.
Intermediate to advanced English proficiency.
Preferred: Azure OpenAI and/or Microsoft Foundry; MongoDB Atlas Vector Search.
Preferred: MCP protocol and agent patterns (ReAct, stateful workflows).
Preferred: LLM security: prompt injection, red teaming, and guardrails.
Preferred: AI FinOps: estimating and optimizing token costs and comparing providers.
Preferred: Node.js/TypeScript.
Preferred: Experience with RPA or integrations with ServiceNow, Microsoft Teams/Graph, or SAP.
Preferred: Business knowledge in healthcare/regulatory affairs (ANVISA), supply chain, or HR benefits.
Preferred: Cloud or AI certifications (e.g., Azure AI Engineer).

Benefits

Comp & perks

Ongoing professional development
Potential project extension or permanent employment after the project
Remote work