AI Engineer

6+ years of software engineering experience, with 3+ years building ML or AI systems in production.Strong Java expertise(Java 11+), including Spring Boot, concurrency, and JVM performance tuning.Solid understanding of ML fundamentals supervised/unsupervised learning, model evaluation, feature engineeringExperience with model serving frameworks (e.g. Triton, BentoML, custom REST wrappers) and integrating Python models into JVM environments (ONNX, PMML, or subprocess approaches).Hands-on with distributed data platforms Apache Kafka, Spark, or Flink.Familiarity with ML tooling MLflow, Kubeflow, or similar.Experience with cloud platforms (AWS, Azure, or GCP) and containerised deployments (Docker, Kubernetes)Comfortable with SQL and columnar data stores; experience with feature stores a plusStrong understanding of software design patterns, distributed systems, and API designNice to HaveExperience in financial services capital markets.Exposure to LLMs, RAG pipelines, or generative AI in an enterprise context.Knowledge of model governance and explainability frameworks.Familiarity with regulatory expectations around model risk management.Architect and develop scalable ML pipelines and AI-powered services in Java, integrating with Python-based model artefacts where needed.Own the full ML lifecycle data ingestion, feature engineering, model serving, monitoring, and retraining.Build and maintain low-latency REST and gRPC microservices that expose model inference to downstream banking applications.Collaborate with data scientists to operationalise models (scikit-learn, XGBoost, PyTorch, etc.) into high-availability JVM-based inference engines.Design robust data pipelines using Kafka, Spark, or Flink for real-time and batch feature computation.Champion engineering best practices code review, testing, CI/CD, and observability - within a regulated financial environment.Contribute to the team's MLOps maturity model versioning, A/B testing, drift detection, and performance dashboards