Sr Machine Learning Engineer Agentic Systems

Develop and optimize machine learning models for various applications. Preprocess and analyze large datasets to extract meaningful insights. Deploy ML solutions into production environments using appropriate tools and frameworks. Collaborate with cross-functional teams to integrate ML models into products and services. Monitor and evaluate the performance of deployed models. Own end-to-end development of agentic systems: planning, task decomposition, tool/function calling, state/memory, multi-step execution, and reliability patterns (fallbacks, retries, idempotency). Design, build, and productionize AI/ML models for risk management, including traditional approaches and neural networks (classification/regression, ranking, anomaly detection, time series, NLP, deep learning; transformers, embeddings, sequence models, representation learning), and integrate them into decisioning workflows. Build and maintain ML pipelines for training, validation, and inference, including feature generation, reproducible experiments, and automated deployment workflows. Implement RAG and grounding pipelines to improve accuracy and auditability (retrieval, reranking, citations/traceability, context controls). Establish evaluation systems: offline datasets, regression suites, online monitoring, drift detection, and error analysis for both agents and models. Define and implement guardrails for agent actions: tool permissions, safe completion rules, policy constraints, and human-in-the-loop patterns where needed. Contribute to data engineering needs: data contracts, scalable pipelines, feature generation, and data quality/lineage checks. Improve runtime performance and operability: latency/cost optimization, observability (metrics/logs/traces), incident response and postmortems. 3+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment. Several years of experience in designing, implementing, and deploying machine learning models. Demonstrated track record owning and shipping multiple production AI/ML systems end-to-end, from problem framing through deployment and iteration. Strong expertise in agentic AI systems, including hands-on experience with LLM-based tool use and at least one of: orchestration frameworks, workflow engines, or agent evaluation frameworks. Strong depth in traditional AI/ML algorithms with practical experience delivering measurable business impact (feature engineering, model training/tuning, evaluation, deployment). Hands-on experience building and optimizing neural networks (PyTorch/TensorFlow), including embeddings/representation learning and model deployment considerations. Solid data engineering skills: SQL fluency, pipeline/ETL design, feature pipelines, and data quality validation. Strong software engineering fundamentals: system design, APIs, testing, CI/CD, and production debugging. Strong business acumen: ability to translate risk goals into metrics, reason about trade-offs, and communicate clearly with technical and non-technical stakeholders. Direct experience in risk management domains (fraud, transaction risk, credit risk, AML, disputes/chargebacks) or other large-scale decisioning systems. Experience with multi-agent architectures, routing policies, planners/state machines, or policy engines. Experience with retrieval optimization (vector search, hybrid search, reranking) and scalable knowledge systems. Experience building experimentation frameworks (A/B testing, counterfactual evaluation) for risk and decisioning.