While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
About Quantiphi:
Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.
Quantiphi has seen 2.5x growth YoY since its inception in 2013, we don’t just innovate - we lead. Headquartered in Boston, with 4,000+ Quantiphi professionals across the globe. As an Elite/Premier Partner for Google Cloud, AWS, NVIDIA, Snowflake, and others, we’ve been recognized with:
17x Google Cloud Partner of the Year awards in the last 8 years.
3x AWS AI/ML award wins.
3x NVIDIA Partner of the Year titles.
2x Snowflake Partner of the Year awards.
We have also garnered top analyst recognitions from Gartner, ISG, and Everest Group.
We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators.
We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023.
Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here!
For more details, visit: Website or LinkedIn Page.
Experience Level: 6+ years of experience
About the Role:
Quantiphi is seeking a seasoned Senior Machine Learning Engineer with extensive experience in designing and implementing advanced machine learning solutions to address complex business challenges. This role focuses on identifying AI/ML use cases with Subject Matter Experts and delivering SaaS product offerings within the Clinical Trials domain. A deep understanding of machine learning algorithms, neural networks, and Large Language Models (LLMs), combined with domain expertise, is essential. The position requires close collaboration with engineers, product teams, and SMEs to build scalable, high-performance AI systems that drive innovation.
What Success Look Like:
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.
Collaborate with product managers, solution architects, and life sciences SMEs to ensure agents are domain-aware, secure, and scalable.
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.
Partner with cross-functional teams—including SMEs, engineers, and product managers—to define technical requirements and deliver AI-driven solutions.
Provide technical leadership and mentorship to junior team members, ensuring best practices in ML and NLP are followed.
Evaluate and integrate cutting-edge NLP technologies and frameworks to enhance LLM capabilities.
Ensure deployed models meet performance, reliability, and compliance requirements.
Oversee data collection, cleaning, and preprocessing of large datasets for LLM training.
Implement robust data pipelines to support continuous model improvement and retraining.
Create and maintain comprehensive documentation for ML models, including design decisions, methodologies, and evaluation metrics.
Communicate complex technical concepts to both technical and non-technical stakeholders.
Monitor and assess deployed ML model performance, identifying areas for improvement and implementing enhancements.
Foster a culture of continuous learning, experimentation, and innovation within the team.
Essential Skills/Qualifications:
Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or equivalent experience.
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.
Familiarity with biomedical data formats and ontologies (e.g., PubMed, patient data).
Ability to translate complex business problems into scalable technical architectures.
Strong collaboration skills with cross-functional teams (SMEs, product, and business stakeholders).
Innovative mindset with a passion for advancing GenAI applications in life sciences.
What We Offer:
Join a high-growth, AI-first digital engineering and transformation company
Work with Fortune 500 clients and cutting-edge market disruptors
Collaborate with a talented, dynamic, and driven team
Gain exposure to the latest technologies in AI, ML, cloud, and data engineering
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
