Senior ML Engineer

We are looking for an ML Engineer/Senior ML Engineer who enjoys taking machine learning systems beyond experimentation and into high-scale, production environments. You will work closely with data scientists and engineering teams to build real-time ML infrastructure, high-throughput inference systems, feature pipelines, model serving platforms, and production observability. This is an engineering-heavy ML role for someone who enjoys solving problems at the intersection of machine learning, distributed systems, platform engineering, and MLOps.

Responsibilities:

• Design and build high-throughput, low-latency real-time and batch feature computation pipelines.
• Partner with data scientists to deploy, profile, optimize, and scale complex ML models for production inference workloads.
• Build end-to-end ML observability, including tracing, monitoring, alerting, and production dashboards.
• Own technical integrations for ML-powered data products and real-time decisioning systems.
• Define and optimize infrastructure for scalable ML deployments.
• Architect systems supporting advanced model deployment patterns such as ensembles, model chaining, shadow deployments, and champion-challenger frameworks.
• Work across internal and external datasets to build impactful ML solutions.
• Apply modern AI/ML frameworks to solve critical business problems.

Requirements:

• 2-5 years of hands-on experience deploying machine learning models in production, preferably real-time environments.
• Bachelor's / Master's / PhD in a STEM discipline from a top-tier institution.
• Strong fundamentals in machine learning, neural networks, and deep learning.
• Strong understanding of distributed systems, real-time computing, and inference-serving architectures.
• Strong programming skills in Python and SQL.
• Exposure to Databricks is highly advantageous.
• Strong software engineering fundamentals and an emphasis on production-quality code.
• Comfortable using modern developer tools, including AI coding assistants such as Claude Code / Codex.

Hands-on exposure to:

• Model Serving: TensorFlow Serving, ONNX.
• Cloud: AWS ecosystem.
• MLOps: MLflow, Kubeflow.
• Containers: Docker.
• ML: PyTorch, scikit-learn.
• Explainability: SHAP, LIME.
• Distributed Processing: Apache Spark.

Strong Plus:

• Experience building ML systems in financial services, fintech, lending, or credit.
• Understanding of model risk management and compliance frameworks.
• Experience operating ML systems where latency, reliability, monitoring, and scale are critical.