Full Stack Engineer

Role overview

Responsibilities

• Develop, test, and maintain backend services, APIs, and web services for Fraud, AML, Finance, and Risk applications.
• Implement SSO, security, and Kafka or MQ messaging integrations according to integration plans.
• 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 through existing CI/CD pipelines; contribute to integration testing and architecture discussions.
• Diagnose and resolve development, test, and production issues, including performance and monitoring concerns.
• Own work 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 leaders, business stakeholders, contractors, and partner teams.
• Apply AI and emerging tools to improve productivity and automation.
• Build data pipelines and scripts, analyze large datasets, identify data-quality issues, and prepare or integrate machine-learning model data and outputs.

Required qualifications

• At least 3 years of software engineering experience using one or more of .NET, Java, or Python.
• At least 2 years of integration experience, including working knowledge of SSO, security, and Kafka or MQ messaging.
• Experience building and consuming APIs and understanding API design.
• Understanding of CI/CD and experience working within existing pipelines; building pipelines from scratch is not required.
• Experience developing and deploying cloud-native applications on AWS, Azure, or GCP.
• Ability to diagnose issues across development, test, and production using monitoring tools such as Splunk or Elastic.
• Ability to document technical designs and data flows and work effectively with technical and business stakeholders.
• Experience remediating security vulnerabilities and supporting application control assessments and data attestations.
• Proficiency in scripting for data extraction, transformation, and validation, SQL, and work with large datasets.

Preferred skills and experience

• Azure is preferred; Docker and Kubernetes experience is a plus.
• Familiarity with audit, risk, or regulatory control processes is nice to have.
• AI agent and LLM experience, including prompt engineering, fine-tuning, RAG, and vector databases, is nice to have.
• Experience with LangChain, AutoGen, CrewAI, function-calling APIs, or data and model orchestration is nice to have.
• Advanced Python expertise for AI/ML work is nice to have; basic Python or scripting for data and automation is required.

Pay and employment

• The posting identifies the role as full-time, regular, and salaried.
• No salary amount or pay cadence is provided.