ML/AI Engineer - Agentic AI & Fraud Analytics (Public Sector)
Confirmed on the employer's own hiring board on Oct 27, 2026. First seen by Alion on Oct 26, 2026. Xtremax scores A on the Alion truth index.
At Xtremax, we're looking for an ML/AI Engineer to build AI-powered systems that help identify emerging fraud risks and improve fraud detection. You'll work with a Data Science and AI team developing the next generation of fraud analytics capabilities, supporting the detection of misuse of public training subsidies.
You'll design and develop two complementary agentic AI systems: one that identifies and prioritises emerging fraud risks from approved information sources, and another that helps translate validated risks into actionable detection logic. The work goes beyond prototyping - you'll build evaluated, maintainable solutions with appropriate human oversight, traceability and responsible-AI safeguards.
Working alongside data scientists, analysts, business users and platform engineers, you'll contribute to production-ready AI workflows while helping establish the engineering practices needed to operate and maintain these systems sustainably.
Responsibilities
- Design end-to-end agentic AI architectures, including workflow orchestration, state management, tool integration and human approval points.
- Develop AI agents that identify, generate and prioritise evidence-linked emerging fraud and abuse risks for officer review.
- Build agent workflows that assess data feasibility, propose risk indicators and features, and support the development of fraud detection logic.
- Integrate AI workflows with enterprise data and analytics components, including AWS Redshift, Glue and PySpark.
- Develop modular, tested and maintainable Python applications, APIs, services and AI workflows suitable for deployment and ongoing operation.
- Implement automated testing and CI/CD workflows for code, prompts, AI components and workflow releases.
- Establish AI evaluation frameworks covering output quality, reproducibility, traceability, security and operational performance.
- Implement responsible-AI safeguards, including access controls, human-in-the-loop approvals, exception handling and protection against prompt injection.
- Build monitoring, dashboards and alerts for system health, data quality, errors, latency, resource utilisation and operating costs.
- Document architecture decisions, evaluation results and operational procedures, and collaborate with stakeholders to transfer knowledge to the internal team.
Requirements
- Degree or diploma in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline.
- Typically 3-5 years of hands-on experience building and deploying ML/AI applications.
- Experience delivering at least one LLM-enabled system beyond the prototype stage into operational use.
- Strong Python software engineering skills, including API development, automated testing, version control and modular application design.
- Experience developing LLM applications involving retrieval, tool use, workflow orchestration or agentic AI.
- Familiarity with frameworks such as LangGraph, CrewAI or equivalent.
- Experience integrating AI/ML solutions with enterprise data platforms, databases, APIs or cloud services.
- Ability to implement systematic evaluation, testing, benchmarking, observability and reproducibility for AI systems.
- Working knowledge of AI governance, security and Responsible AI practices, including human oversight, access management and auditability.
- Strong analytical thinking and the ability to translate business requirements into maintainable AI-enabled solutions.
Preferred
- Experience with AWS services such as Bedrock, Redshift, Glue, S3 or Lambda.
- Experience with PySpark, Databricks or large-scale data processing and feature engineering.
- Familiarity with Docker, MLflow, CI/CD pipelines or other MLOps tools.
- Experience with RAG, graph-based workflows, prompt management or AI observability.
- Experience in fraud detection, risk, compliance, investigations or decision-support systems.
- Familiarity with public-sector data governance, security and compliance requirements.
- Experience working with data scientists, engineers, analysts and business stakeholders.
Benefits
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