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Support Analyst Fintech Sr/Ssr
Brindar soporte técnico especializado en la resolución de incidencias relacionadas con entornos de bases de datos y aplicaciones, dentro del equipo de soporte Fintech.
Responsabilidades principales:
Diagnóstico y resolución de incidencias técnicas.
Monitoreo proactivo de servicios, con análisis detallado de logs y métricas.
Seguimiento de incidentes hasta su resolución.
Automatización de tareas mediante scripting.
Pruebas y depuración de APIs REST.
Requisitos técnicos:
Monitoreo y análisis de logs: Experiencia con herramientas como Kibana y Dynatrace.
Sistemas y scripting: Conocimientos básicos de Linux (comandos esenciales) y manejo solido de Scripting (Bash/Python).
Bases de datos: Experiencia intermedia/avanzada en PL/SQL sobre Oracle (consultas, procedimientos almacenados, optimización de queries).
Experiencia intermedia/avanzada con Python
APIs: Uso de Postman para pruebas y depuración de APIs REST.
Senior Machine Learning Engineer
Senior Machine Learning Engineer, Recommendation Systems
Join an ML team to design, train and operate recommendation and ranking models in production on AWS + Databricks/PySpark, from feature pipelines to serving, monitoring and retraining.
Must-Haves
Recommendation Systems
Collaborative filtering with implicit and explicit feedback, matrix factorization and ALS, including tuning rank, regularization and confidence weighting.
Two-stage architectures: candidate generation (ALS, two-tower, ANN) and ranking (GBDT or deep models).
Negative sampling, cold-start handling and fallback strategies.
Offline evaluation with NDCG, MAP, Precision@K and Recall@K using temporal splits to avoid leakage, plus online validation through A/B testing on business metrics (CTR, conversion, revenue).
Large-Scale Spark/PySpark
Distributed processing on real-world volumes (TBs or billions of events) that you can quantify.
Partitioning, broadcast vs. shuffle joins, salting for data skew, and caching and persistence.
Reading execution plans and the Spark UI, AQE, and memory and parallelism tuning.
Cluster and job cost optimization.
Production ML and Deep Learning
Models in PyTorch or TensorFlow that you designed, trained and deployed yourself.
Batch and real-time serving with latency requirements, containerization and CI/CD.
Monitoring for data drift and model performance, with automated retraining pipelines.
Technical Foundations
Advanced Python and SQL with production-quality code (testing, typing, code review).
ML deployment on AWS: SageMaker, ECS/Fargate, Lambda, S3, Glue, Athena, DynamoDB and CloudWatch.
Strong fundamentals in algorithms, data structures and system design.
Nice to Have
Databricks: Jobs, Workflows, Delta Lake and Unity Catalog.
Orchestration with Airflow or similar.
MLflow for experiment tracking and model registry.
Feature stores and serving with Docker + FastAPI.
Personalization in e-commerce, retail or marketplaces.
LLMs, RAG or AI agents (a plus, but not a substitute for the core requirements).
Conditions
100% remote
English C1
5+ years of experience in ML Engineering
Tech Lead — Conversational AI Agents (AWS)
Tools
Amazon Bedrock (Knowledge Bases, Guardrails, AgentCore), Amazon OpenSearch Serverless, AWS Lambda, API Gateway, Amazon ECS / EKS, AWS Step Functions, VPC + PrivateLink, IAM + SCPs, WAF + Shield, CloudWatch + X-Ray, AWS Organizations / Control Tower, Cognito.
Frameworks
RAG with Bedrock Knowledge Bases evolving to AgentCore; custom agent orchestration with LangGraph / LangChain on ECS; C4 modeling and ADRs as design artifacts; AWS Well-Architected Framework; external agent distribution via OpenAI GPT Store (GPT Actions) and Anthropic (Claude integrations).
Type of Project
Technical leadership and solution architecture for a bank's conversational-AI agents on AWS — owning HLD/LLD design, the RAG → AgentCore productionization roadmap, and integration between Bedrock, Lambda, API Gateway and core banking systems. Acts as the technical point of contact across governance committees (Architecture, Cybersecurity, Infrastructure, Data & Privacy/LGPD, Risk, Compliance), with strong focus on security (Guardrails, OWASP LLM Top 10, prompt-injection controls, least-privilege IAM), and drives the squad from design to go-live across the superapp and external channels.
Senior DevOps (Azure)
Tools
Azure (mandatory), Terraform, CloudFormation, Azure DevOps, Docker, Kubernetes / ECS, Linux (RHEL and Debian-based distros), and scripting with Bash, PowerShell, Python, and Groovy.
Frameworks
CI/CD pipelines with Azure DevOps, Infrastructure as Code (Terraform, CloudFormation), and container orchestration (Kubernetes / ECS), applied with a strong focus on DevOps best practices and quality.
Type of Project
Deploying and managing code changes and automating operational processes quickly, accurately, and securely across cloud-based infrastructure. Scope and design day-to-day architecture/infrastructure decisions, operate systems reliably, and manage all relevant assets (pipelines, logs, keys, scripts, deployment procedures) in an agile, collaborative environment. Fintech/banking domain experience is a plus, with a holistic view of the SDLC, virtualization, networking, and container technologies.