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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.
Senior Machine Learning Engineer
Tools
Python, SQL, AWS / GCP / Azure, Spark or Databricks, Docker, Kubernetes, Kafka (or Kinesis / Flink), Terraform.
Frameworks
PyTorch and/or TensorFlow (deep learning), along with MLOps practices for model deployment, serving, and monitoring.
Type of Project
Design and productionization of Machine Learning solutions at scale: ML/DL models, batch and real-time data pipelines, and the services and infrastructure to operate them reliably. Applied across domains such as predictive modeling, recommendation systems, computer vision, NLP, and information retrieval — with GenAI (LLMs / RAG / agents) as a plus.