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Senior ML Engineer

Quantiphi

Salary not specified
Sep 16, 2025
Remote, US
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Quantiphi is seeking a Senior Machine Learning Engineer to design and implement advanced machine learning solutions to address complex business challenges, specifically focusing on identifying AI/ML use cases and delivering SaaS product offerings within the Clinical Trials domain.

Requirements

  • 6–8 years of experience in machine learning, including hands-on work with LLMs.
  • Proven track record of developing and deploying large-scale ML models in production environments.
  • Strong programming skills in Python and JavaScript/TypeScript.
  • Hands-on experience with GCP Agentspace, Vertex AI, LangChain, LlamaIndex, or agent orchestration frameworks.
  • Deep understanding of LLM fine-tuning, Retrieval-Augmented Generation (RAG), embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS).
  • Experience with cloud-native architectures (microservices, Docker, Kubernetes).
  • Exposure to life sciences workflows in commercial settings.

Responsibilities

  • Develop and maintain the architecture for machine learning models.
  • Architect and implement autonomous AI agents on GCP Agentspace for healthcare and life sciences workflows.
  • Translate domain problems (e.g., commercials, sales, marketing use cases) into agentic workflows.
  • Develop agent orchestration frameworks enabling chaining, collaboration, and governance of multiple agents.
  • Define and enforce best practices, architectural standards, and guidelines for ML model development and deployment.
  • Lead the design, training, and fine-tuning of LLMs, neural networks, and other ML models to meet specific business needs.
  • Optimize ML models for performance, scalability, and efficiency in production environments.

Other

  • Experience Level: 6+ years of experience
  • Work Location: USA (East or Central)
  • Strong collaboration skills with cross-functional teams (SMEs, product, and business stakeholders).
  • Innovative mindset with a passion for advancing GenAI applications in life sciences.
  • Communicate complex technical concepts to both technical and non-technical stakeholders.