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 with a strong background in life sciences, pharmaceuticals, or healthcare. 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 GCP).

* 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 in healthcare.

* Ensure data integrity, security, and interoperability with medical data standards (HL7, FHIR, CDISC, OMOP, etc.).

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, GCP 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).