Full Stack Engineer

Role overview

What you will do

• Develop, test, and maintain services, APIs, and web services for Fraud, AML, Finance, and Risk applications.
• Implement SSO, security, and Kafka or MQ messaging integrations according to the integration plan.
• Work primarily with .NET and use Python for scripting and automation.
• Develop and deploy cloud-native AI/ML solutions on AWS, Azure, or GCP, using technologies such as Kubernetes and Docker.
• Build, test, and deploy changes within existing CI/CD pipelines; creating pipelines from scratch is not expected.
• Contribute to integration testing, development, and architecture discussions for assigned components.
• Diagnose and resolve issues in development, test, and production environments; support performance monitoring.
• Own components from development through production, including monitoring and support.
• Document technical designs, data flows, changes, and support procedures.
• Remediate vulnerabilities and support application control assessments and data attestations.
• Collaborate with IT leadership, business stakeholders, contractors, and partner teams.
• Build data pipelines and scripts; analyze datasets and data quality; prepare datasets, run experiments, and integrate model outputs.

Required qualifications

• At least 3 years of software engineering experience with one or more of .NET, Java, or Python.
• At least 2 years of integration experience, including SSO, security, and Kafka or MQ messaging components.
• Experience building and consuming APIs and working within existing CI/CD pipelines.
• Experience developing and deploying cloud-native applications on AWS, Azure, or GCP, emphasizing scalability, reliability, and efficiency.
• Working knowledge of cloud platforms; Azure is preferred. Docker and Kubernetes experience is a plus.
• Ability to diagnose issues across development, test, and production using tools such as Splunk or Elastic.
• Ability to document technical designs and data flows and work effectively with technical teams and business stakeholders.
• Experience remediating security vulnerabilities and supporting application control assessments and data attestations.
• Proficiency in SQL and scripting for data extraction, transformation, validation, and large-dataset work.
• Familiarity with audit, risk, or regulatory controls is nice to have.

Additional preferred experience

• Nice to have: autonomous AI agents and LLM knowledge, including transformers, tokenization, embeddings, prompt engineering, fine-tuning, RAG, and vector databases.
• Nice to have: LangChain, AutoGen, CrewAI, function-calling API design, data orchestration, and model management pipelines.
• Deep Python expertise for AI/ML is preferred; basic Python scripting for data and automation is required.

Employment and compensation

• The position is full-time, regular, salaried, with 37.5 work hours per week.
• The posting describes total rewards and possible bonuses, benefits, commissions, and stock where applicable; no specific salary amount or pay cadence is stated.

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