Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.
About the role
- AI/ML Engineer building enterprise GenAI capabilities for Safe-Guard Products International. Developing scalable AI solutions across various platforms and improving employee workflows.
Responsibilities
- Design and implement enterprise GenAI platform components, including:
- LLM orchestration (LangChain, LangGraph) and provider abstraction (Azure OpenAI, AWS Bedrock, GCP Vertex).
- Prompt and workflow templates.
- Evaluation frameworks and telemetry.
- Guardrails for safety, compliance, and data protection.
- Define standardized GenAI patterns that product and engineering teams can reuse across the organization.
- Partner with architecture, security, and compliance teams to ensure GenAI solutions align with enterprise policies and Responsible AI principles.
- Establish technical standards and best practices for GenAI development across onshore and offshore teams.
- Support experimentation by business users while ensuring guardrails, monitoring, and auditability.
- Work with business stakeholders to identify high-impact employee workflows where GenAI can improve productivity, decision-making, and quality.
- Partner with IT and business teams to expand and optimize the use of M365 Copilot across employee roles (operations, customer service, leadership, support teams).
- Design and implement integrations that connect enterprise data, knowledge bases, and workflows to M365 Copilot using approved patterns.
- Build ML and GenAI solutions that support multiple business domains, including:
- Claims loss mitigation and fraud detection.
- Customer and agent experience.
- Operational efficiency and internal productivity.
- Combine classical ML, rules engines, and LLMs into hybrid systems where appropriate.
- Ensure solutions provide explainability, traceability, and measurable business value.
- Build enterprise-grade RAG pipelines over structured and unstructured data (policies, procedures, internal documentation, historical records).
- Implement document ingestion, chunking, embeddings, re-ranking, and citation-based outputs.
- Ensure GenAI responses are grounded, accurate, and auditable.
- Define and implement evaluation strategies for ML and GenAI solutions, including:
- Accuracy, precision/recall, hallucination rates.
- Latency and cost metrics.
- User adoption and satisfaction.
- Build monitoring and feedback loops to continuously improve AI systems.
- Apply Responsible AI practices: PII protection, access controls, logging, and audit trails.
Requirements
- 3–4 years of experience in AI/ML engineering, applied data science, or related roles.
- Strong proficiency in Python and SQL.
- Solid foundation in machine learning (classification, anomaly detection, model evaluation).
- Hands-on experience with GenAI systems, including:
- Prompt design and testing.
- Embeddings and vector search.
- Retrieval-Augmented Generation (RAG).
- Structured outputs and validation.
- Experience building and deploying APIs/services (FastAPI or similar).
- Familiarity with containerization (Docker) and CI/CD.
- Experience working in cloud environments (Azure, AWS, and/or GCP).
- Strong communication skills and comfort working in product-facing and enablement roles.
- Experience building internal platforms, shared services, or enablement frameworks.
- Exposure to M365 Copilot extensibility or enterprise productivity tooling.
- Familiarity with ML/LLMOps tools (model registries, experiment tracking, prompt/version management).
- Knowledge of Responsible AI, governance, and enterprise data privacy practices.
- Experience in regulated industries (insurance, auto warranty, financial services).
Benefits
- Medical, Dental, and Vision Insurance
- Flexible Spending Account
- Health Savings Account
- 401(k) Plan with Company Match
- Company-paid Short-Term and Long-Term Disability
- Company-paid Life Insurance
- Paid Holidays and Vacation
- Employee Referral Program
- Employee Assistance Program
- Wellness Programs
- Paid Community Service Opportunities
- Tuition Reimbursement
- Ongoing Training & Personal Development
- And More!
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