Senior Applied AI / ML Engineer Agentic & Intelligent Automation

We are looking for a Senior Applied AI / ML Engineer to design and build production-grade AI solutions for complex, high-volume transactional and regulated environments. The role combines machine learning, GenAI/LLMs, agentic workflows and strong software/data engineering.

The ideal candidate is a hands-on engineer who can take AI solutions beyond experimentation and build scalable, explainable and auditable production systems.

Key Responsibilities:

- Design and develop ML solutions for anomaly detection, classification, prediction and root-cause analysis.

- Build AI capabilities that can explain anomalies, investigate potential causes and recommend corrective actions.

- Develop controlled agentic AI workflows using LLMs, tool calling, APIs and enterprise data sources.

- Build RAG-based solutions using structured and unstructured enterprise data.

- Integrate AI/ML services into existing transactional and data platforms.

- Develop production-grade Python services, APIs and data-processing components.

- Implement appropriate guardrails, confidence scoring, explainability and human-in-the-loop controls.

- Establish ML/LLM evaluation, monitoring, tracing and auditability.

- Work closely with data, platform, product and domain teams to take AI use cases from concept through production.

Essential Skills:

- 712 years of software/data engineering experience with strong recent experience in Applied AI/ML.

- Advanced Python and strong production software-engineering practices.

- Strong ML knowledge including classification, regression, anomaly detection and gradient-boosting techniques.

- Experience with explainable AI techniques such as SHAP/feature attribution.

- Hands-on experience with LLMs, structured outputs, tool/function calling and prompt engineering.

- Experience building agentic workflows using LangGraph, Semantic Kernel, LlamaIndex or similar frameworks.

- Strong understanding of RAG, embeddings, vector/hybrid search and retrieval evaluation.

- Strong SQL and data engineering skills; experience with APIs and event/streaming architectures such as Kafka.

- Experience deploying AI/ML workloads on AWS or Azure.

- Understanding of MLOps/LLMOps, model monitoring, evaluation, observability and CI/CD.

Highly Desirable:

- Experience within banking, capital markets, payments, financial crime, risk, reconciliation, regulatory reporting or other regulated transactional environments would be highly beneficial.

What we're looking for:

This is not a pure data-science or chatbot-development role. We are looking for an engineer who combines ML depth, modern GenAI/agentic capabilities and strong production engineering skills and can build AI systems where accuracy, explainability, security and auditability matter.