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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Python backend development and SQL data analysis, with a strong focus on building enterprise-grade GenAI solutions and workflows for financial services. Proficient in integrating AI applications with enterprise systems and implementing robust validation and evaluation practices.
Highest-signal resume keywords
Python Backend DevelopmentSQL Data AnalysisLLM/GenAI Application DevelopmentWorkflow Orchestration FrameworksCloud Platform Deployment
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLFastAPILLM EvaluationRoot-Cause AnalysisTestingCode ReviewCIData AnalysisOrchestration Frameworks
Soft Skills
Analytical SkillsCollaborationDebuggingCommunication
Tools & Technologies
AWSGCPAzureDockerKubernetesTerraformAI Observability FrameworksGraph RAGVector DatabasesHybrid Search
Industry Keywords
Financial ServicesRegulatory ReportingCapital MarketsTrade LifecyclePost-Trade ProcessingTransaction ReportingReconciliationsException ManagementRisk and ControlsEMIR
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonReactSQLTerraform
About the role
Key responsibilities & impact- Design, build, and deploy enterprise-grade GenAI and Agentic AI solutions for complex financial services workflows
- Build workflows for exception classification, triage, root-cause analysis, impact assessment, and remediation recommendations
- Develop agentic workflows with orchestration, tool/function calling, state management, and human-in-the-loop validation
- Correlate new exceptions with historical issues, root causes, business rules, and transaction/data attributes
- Build Python and SQL services to query, transform, and analyze large structured enterprise datasets
- Develop reusable tools and services for AI-agent retrieval, investigation, analysis, and workflow execution
- Integrate AI applications with enterprise APIs, databases, workflow/ticketing platforms, and internal data sources
- Apply RAG and context retrieval over regulatory documents, historical knowledge, and issue repositories
- Implement confidence scoring, validation, guardrails, and traceability for AI-generated outcomes
- Build backend services and APIs using Python/FastAPI and deploy them on a major cloud platform
- Implement testing, logging, evaluation, observability, and production engineering practices
- Collaborate with onshore and offshore engineers, architects, business analysts, data engineers, and application teams
Requirements
What you’ll need- Strong hands-on proficiency in Python backend development and production-grade services with frameworks such as FastAPI
- Strong SQL and data analysis skills with large structured datasets
- Proven experience building LLM/GenAI applications beyond proof-of-concept chatbots
- Hands-on experience with an agent or workflow orchestration framework such as LangGraph, Google ADK, CrewAI, AutoGen, Semantic Kernel, or equivalent
- Understanding of tool/function calling and reusable agent tools
- Experience with RAG and retrieval techniques
- Experience implementing LLM evaluation, guardrails, and validation
- Software engineering fundamentals including Git, testing, code review, and CI
- Experience deploying backend services on a major cloud platform; AWS preferred, GCP or Azure considered
- Strong analytical, debugging, and root-cause analysis skills
- Very good English and ability to collaborate across distributed engineering and business teams
- Pragmatic understanding of deterministic logic versus LLM use
- Experience level: Senior-level
- Nice to have: financial services experience, including regulatory reporting, capital markets, trade lifecycle, post-trade processing, transaction reporting, reconciliations, exception management, or risk and controls
- Nice to have: familiarity with EMIR, MiFID II, or SFTR
- Nice to have: Model Context Protocol (MCP) and reusable agent tool interfaces
- Nice to have: Knowledge Graphs, Graph RAG, or data lineage
- Nice to have: vector databases, hybrid search, or re-ranking
- Nice to have: AI observability and evaluation frameworks
- Nice to have: AWS AI services such as Bedrock and SageMaker; Docker, Kubernetes, and Terraform
- Nice to have: translating requirements into specifications, tasks, and acceptance criteria
- Nice to have: Claude Code or similar AI coding assistants
- Nice to have: basic React or frontend integration experience
- Must answer whether based in Brazil
Benefits
Comp & perks- People-first company culture
- Supportive, trusted, and empowering work environment
- Team collaboration and long-term partnership focus
- Opportunity to work on meaningful products and AI solutions
- Opportunity to collaborate with global/onshore and offshore teams