Senior Machine Learning Engineer
- USA
- Permanent
- Full-time
- Collaborate with data scientists to deploy and maintain machine learning models for forecasting, anomaly detection, NLP analysis and customer outcome prediction. Take ownership of the full development lifecycle : including development, CI/CD pipelines, containerization, orchestration, and performance monitoring with tools like MLflow.
- Build a strong foundation of scalable and reusable infrastructure to support Finance's ML/AI experimentation. Contribute SDKs, APIs, and clear documentation to streamline ML/LLM adoption and provide shared services in line with the Finance AAI's roadmap.
- Design and implement LLM-driven workflows and autonomous agents tailored to Finance use cases such as narrative generation, AI copilots, and RAG-based insights. Apply techniques like prompt engineering, fine-tuning, and embedding search using open-source libraries including LangChain, Hugging Face, and OpenAI APIs. Confidently validate model performance using standard benchmarks and custom metrics.
- Champion strong engineering practices through mentorship and technical guidance, ensuring systems are aligned with data governance and regulatory standards. Collaborate with central teams at Hubspot to enable ML/AI best practices
- Engage in code reviews, testing, and documentation efforts to uphold code quality and maintainability. Mentor junior engineers and data scientists to strengthen their software development skills, optimize algorithms, and stay current with advancements in the field.
- Degree in Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
- Expert knowledge of machine learning and AI techniques, with a strong track record of selecting appropriate methods and successfully deploying multiple models in production, including full lifecycle ownership, development, CI/CD, monitoring, and maintenance
- Strong Python & SQL skills with deep experience in ML/AI libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face, CrewAI and LangChain.
- Hands-on experience with LLM deployment (workflows & agents), vector search, and RAG pipelines at scale.
- Familiarity with Java is a big plus, especially for internal service integration.
- Knowledgeable in model monitoring, alerting, and evaluation techniques.
- Proficient developing & working with API's & standard ML deployment stack: Git, Docker, Kubernetes, FastAPI, etc.
- Capable of independently leading ML/AI projects from concept to production.
- Excellent communicator, able to translate complex technical ideas to diverse audiences. Collaborative and creative, thrives in fast-paced, iterative environments.
- Familiarity with React framework, and RPC functions to expose back-end systems to front-end clients.
- Experience working with Kafka or other stream based processing architecture.
- Familiarity with emerging LLM architectures and techniques in LLM space like Graph RAG, MCP, A2A, Long/Short term memory.
- Collaborate with data scientists to deploy and maintain machine learning models for forecasting, anomaly detection, NLP analysis and customer outcome prediction. Take ownership of the full development lifecycle : including development, CI/CD pipelines, containerization, orchestration, and performance monitoring with tools like MLflow.
- Build a strong foundation of scalable and reusable infrastructure to support Finance's ML/AI experimentation. Contribute SDKs, APIs, and clear documentation to streamline ML/LLM adoption and provide shared services in line with the Finance AAI's roadmap.
- Design and implement LLM-driven workflows and autonomous agents tailored to Finance use cases such as narrative generation, AI copilots, and RAG-based insights. Apply techniques like prompt engineering, fine-tuning, and embedding search using open-source libraries including LangChain, Hugging Face, and OpenAI APIs. Confidently validate model performance using standard benchmarks and custom metrics.
- Champion strong engineering practices through mentorship and technical guidance, ensuring systems are aligned with data governance and regulatory standards. Collaborate with central teams at Hubspot to enable ML/AI best practices
- Engage in code reviews, testing, and documentation efforts to uphold code quality and maintainability. Mentor junior engineers and data scientists to strengthen their software development skills, optimize algorithms, and stay current with advancements in the field.
- Degree in Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
- Expert knowledge of machine learning and AI techniques, with a strong track record of selecting appropriate methods and successfully deploying multiple models in production, including full lifecycle ownership, development, CI/CD, monitoring, and maintenance
- Strong Python & SQL skills with deep experience in ML/AI libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face, CrewAI and LangChain.
- Hands-on experience with LLM deployment (workflows & agents), vector search, and RAG pipelines at scale.
- Familiarity with Java is a big plus, especially for internal service integration.
- Knowledgeable in model monitoring, alerting, and evaluation techniques.
- Proficient developing & working with API's & standard ML deployment stack: Git, Docker, Kubernetes, FastAPI, etc.
- Capable of independently leading ML/AI projects from concept to production.
- Excellent communicator, able to translate complex technical ideas to diverse audiences. Collaborative and creative, thrives in fast-paced, iterative environments.
- Familiarity with React framework, and RPC functions to expose back-end systems to front-end clients.
- Experience working with Kafka or other stream based processing architecture.
- Familiarity with emerging LLM architectures and techniques in LLM space like Graph RAG, MCP, A2A, Long/Short term memory.