We're looking for a Senior Machine Learning Engineer to help design, deploy, and scale machine learning solutions that drive product innovation and business intelligence. In this high-impact role, you’ll work with structured and unstructured data, implement cutting-edge ML models (including LLMs), and help define engineering best practices in a fast-moving, collaborative environment.

You’ll partner closely with engineering, product, and analytics teams to develop end-to-end ML infrastructure and real-time solutions that power critical features and insights across the organization.

What You’ll Do

• Build and deploy ML models across supervised, unsupervised, and semi-supervised learning paradigms.
• Leverage open-source and proprietary large language models (LLMs) for applications like RAG, summarization, and classification.
• Design and maintain scalable ML pipelines, working across production environments using cloud infrastructure and containerized tools.
• Develop using Python, SQL, and ML libraries like PyTorch, Hugging Face Transformers, scikit-learn, and LangChain.
• Drive model experimentation, monitoring, and traceability with tools like MLflow, Datadog, or Langfuse.
• Collaborate with cross-functional teams to translate product and business requirements into ML-driven features.
• Own full project lifecycles—from prototyping through to deployment and observability.

What You Bring

• 5+ years of experience in software or ML engineering, including 3+ years deploying production ML systems.
• Expertise in Python and SQL, with strong experience in deep learning frameworks and LLM toolkits.
• Hands-on experience building and scaling solutions with LLMs (e.g., GPT, BERT), including embedding and prompt engineering.
• Proficiency with containerized deployment (Docker/Kubernetes) and cloud platforms (AWS, GCP, or equivalent).
• Strong foundations in data engineering, feature generation, and working with unstructured/complex data.
• Strong communication skills and a collaborative mindset—comfortable working across engineering, product, and analytics.

Bonus Points For

• Familiarity with MLOps platforms (MLflow, SageMaker, Kubeflow).
• Experience with A/B testing frameworks and causal inference.
• Background in clustering, anomaly detection, or computer vision.
• Experience with modern data stores (Snowflake, Redis, PostgreSQL, Milvus).
• Exposure to streaming frameworks (Kafka, Flink, NATS).