Quantiphi is seeking a Senior Machine Learning Engineer to design and implement advanced machine learning solutions to address complex business challenges, 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).
- Familiarity with biomedical data formats and ontologies (e.g., PubMed, patient data).
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)
- Collaborate with product managers, solution architects, and life sciences SMEs to ensure agents are domain-aware, secure, and scalable.
- Partner with cross-functional teams—including SMEs, engineers, and product managers—to define technical requirements and deliver AI-driven solutions.
- Communicate complex technical concepts to both technical and non-technical stakeholders.
