Machine Learning Engineer

Role overview

Responsibilities

• check_circle Develop and train ML models — including sequence, embedding, and classification models — on large-scale financial and behavioral data.
• check_circle Build feature and data pipelines that turn raw event data into training-ready datasets, and keep training and serving features consistent.
• check_circle Design offline and online evaluation for models and agentic workflows: success metrics, backtests, A/B tests, error tracing, and regression suites.
• check_circle Take models to production and own them there — serving infrastructure, latency and cost tuning, retraining loops, and monitoring for drift and performance degradation.
• check_circle Fine-tune and adapt LLMs for internal use cases, and build the orchestration around them: prompting, memory and context pipelines, retrieval, and tool integrations.
• check_circle Build backend services and RESTful APIs in Python that expose models and agentic applications to internal tools and product surfaces.
• check_circle Instrument pipelines for observability — logging, tracing, and distributed monitoring across model and agent workflows.
• check_circle Collaborate cross-functionally with ML engineers, data scientists, and product to shape intelligent and safe AI features.
• check_circle Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field, or equivalent experience
• check_circle 2+ years of industry experience building and shipping ML systems.
• check_circle Strong Python and hands-on experience with PyTorch and the standard ML stack (NumPy, pandas, scikit-learn).
• check_circle Experience using AI-assisted development tools (e.g., GitHub Copilot, Cursor, ChatGPT, or similar tools) as part of your software development workflow
• check_circle Solid grounding in ML fundamentals: model architecture choices, training dynamics, regularization, and how to diagnose a model that isn't learning
• check_circle Experience with large-scale data processing (Spark, Databricks, or similar) and feature engineering on production data.
• check_circle Experience designing evaluation for ML systems and LLM behavior — metrics, automated checks, offline test harnesses, and behavioral regression suites
• check_circle Working knowledge of LLM APIs (e.g., OpenAI, Claude), prompt engineering, and at least one agentic framework or custom equivalent.
• check_circle Experience with API design, async workflows, and production database usage (SQL or NoSQL).
• check_circle Clear communication and a collaborative mindset.
• check_circle Experience with LLM fine-tuning using frameworks such as Unsloth, Axolotl, LLaMA-Factory, or HuggingFace PEFT/TRL, including parameter-efficient methods (LoRA/QLoRA) is a plus
• check_circle Experience with distributed training or representation learning is a plus.
• check_circle Familiarity with MLOps tooling for experiment tracking, feature stores, or model registries (MLflow, Weights & Biases, Feast) is a plus.
• check_circle Familiarity with vector stores (e.g., Weaviate, Pinecone, Qdrant) is a plus
• check_circle Knowledge of OpenTelemetry or similar observability frameworks is a plus
• check_circle Exposure to container-based deployment or serverless environments (Docker, AWS Lambda, etc.).
• check_circle Background in fintech, fraud, risk, or credit modeling is a plus

About the company