About us:
Intuitive is an innovation-led engineering company delivering business outcomes for 100 s of Enterprises globally. With the reputation of being a Tiger Team & a Trusted Partner of enterprise technology leaders, we help solve the most complex Digital Transformation challenges across following Intuitive Superpowers:
Modernization & Migration
- Application & Database Modernization
- Platform Engineering (IaC/EaC, DevSecOps & SRE)
- Cloud Native Engineering, Migration to Cloud, VMware Exit
- FinOps
Data & AI/ML
- Data (Cloud Native / DataBricks / Snowflake)
- Machine Learning, AI/GenAI
Cybersecurity
- Infrastructure Security
- Application Security
- Data Security
- AI/Model Security
SDx & Digital Workspace (M365, G-suite)
- SDDC, SD-WAN, SDN, NetSec, Wireless/Mobility
- Email, Collaboration, Directory Services, Shared Files Services
Intuitive Services:
- Professional and Advisory Services
- Elastic Engineering Services
- Managed Services
- Talent Acquisition & Platform Resell Services
About the job:
Title: Delivery Leader AI/ML
Start Date: Immediately
# of Positions: 5
Position Type: FTE
Location: Remote across USA/ Canada
Role Overview
We are seeking an experienced Practice Lead AI/ML. The ideal candidate will combine technical leadership in AI/ML architecture with domain expertise in medical or pharma data, ensuring the successful delivery of AI-driven solutions that enhance research, clinical, and commercial outcomes.
The role demands a unique blend of strategic delivery management, hands-on technical acumen, and cross-functional collaboration with data scientists, clinicians, and business stakeholders.
Key Responsibilities
Delivery Leadership
- Lead end-to-end delivery of AI/ML projects within the medical/pharma domain, ensuring timelines, quality, and compliance.
- Define delivery roadmaps, sprint plans, and milestones aligned with business and regulatory requirements.
- Oversee cross-functional teams including data engineers, ML scientists, and domain SMEs.
- Monitor project performance, proactively manage risks, and ensure adherence to GxP and data governance standards.
Architecture & Technical Oversight
- Design scalable AI/ML architectures leveraging cloud platforms (AWS, Azure, or Google Cloud Platform).
- Guide the development of machine learning pipelines for clinical data, RWE (Real-World Evidence), drug discovery, or patient analytics.
- Evaluate and implement frameworks for NLP, computer vision, or predictive analytics.
Stakeholder & Business Collaboration
- Act as a bridge between data science, IT, and clinical/business stakeholders.
- Translate business needs into technical solutions and ensure clear communication across teams.
- Collaborate with medical affairs, R&D, and regulatory teams to align AI solutions with scientific and ethical standards.
Innovation & Governance
- Drive adoption of AI/ML best practices, MLOps frameworks, and reusable assets.
- Stay abreast of emerging trends in AI for life sciences and proactively recommend improvements.
- Ensure compliance with data privacy and ethical AI principles (HIPAA, GDPR, etc.).
Required Skills & Experience
- Education: Bachelor s or master s in computer science, Biomedical Engineering, Data Science, or related field. PhD preferred.
- Experience: 10+ years of total experience, with at least 4 5 years in AI/ML delivery leadership.
- Proven track record in delivering AI/ML projects in pharma, healthcare, or medical research domains.
- Strong understanding of medical data sources (EHR, clinical trials, RWD, genomics, etc.).
- Proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps tools (Kubeflow, MLflow, Airflow).
- Expertise in cloud services (AWS SageMaker, Azure ML, Google Cloud Platform Vertex AI).
- Familiarity with compliance standards and clinical data handling.
- Excellent communication, stakeholder management, and leadership skills.
Nice to Have
- Experience with LLMs, GenAI, or multimodal AI in healthcare.
- Knowledge of Ontologies, Knowledge Graphs, or semantic modeling in biomedical contexts.
- Exposure to regulatory AI applications (e.g., drug safety, pharmacovigilance).
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