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Sr. Data Engineer & AI Architect

00069590071


AI Architect – Data Engineer (Clinical Data Management Study Build Automation)

Job Summary

Cognizant is seeking a highly experienced AI Architect – Data Engineer to lead the technical architecture and data engineering strategy for an AI-driven Clinical Data Management (CDM) Study Build Automation initiative within the Life Sciences domain. This hybrid role will be responsible for designing, implementing, and validating the end-to-end solution architecture that enables automated Study Build capabilities within Veeva CDMS through Large Language Models (LLMs), Knowledge Graph technologies, and intelligent browser-based automation.

The successful candidate will serve as the primary technical leader across Foundation POC, Evolved POC, and production-scale deployment phases, partnering closely with customer stakeholders including IT, AI/ML Center of Excellence, Data Engineering, and Clinical Study Setup teams. This role combines expertise in AI architecture, data engineering, integration design, metadata management, and Clinical Data Management systems.


Work Model

Location: Hybrid

This role requires a combination of onsite and remote work. The associate will regularly collaborate onsite with customer stakeholders, including IT, AI/ML Center of Excellence, Data Engineering, and Clinical Study Setup teams, while also supporting project activities remotely as needed. Occasional travel may be required based on project and client needs.


Key Responsibilities

Solution Architecture & Technical Leadership

  • Design and own the end-to-end architecture for AI-driven Study Build Automation solutions.
  • Define integration architectures connecting workflow orchestration platforms, Knowledge Graph technologies, approved LLM services, and Veeva CDMS/CTMS ecosystem APIs.
  • Develop architecture blueprints, technical roadmaps, and implementation strategies that support scalability, performance, security, and compliance requirements.
  • Perform technology stack assessments and produce architecture alignment, gap analysis, and remediation recommendations.

AI & Automation Solution Design

  • Architect AI-powered Specification Agents responsible for generating:
    • eCRF Specifications
    • Edit Check Specifications
  • Design Build Agent frameworks utilizing browser automation and Playwright-based hybrid automation approaches.
  • Ensure AI solutions align with clinical standards, governance requirements, and operational workflows.
  • Review and validate AI-generated outputs against established study standards and customer requirements.

Knowledge Graph & Data Engineering

  • Design and implement the Knowledge Graph schema supporting clinical metadata standards and study build automation processes.
  • Develop and own data ingestion frameworks and pipelines feeding the Knowledge Graph ecosystem.
  • Seed, validate, and maintain Knowledge Graph content accuracy and consistency against customer Study Standards.
  • Ensure effective linkage between study standards, metadata assets, specifications, and automation workflows.

Integration & Platform Engineering

  • Establish and validate end-to-end connectivity across all solution components.
  • Lead integration testing and troubleshoot issues involving:
    • APIs
    • Data pipelines
    • Environment configurations
    • Metadata repositories
    • Clinical systems
  • Resolve data quality, configuration, and interoperability challenges across upstream and downstream platforms.
  • Support environment setup, access provisioning, and technical readiness activities.

Proof of Concept (POC) Delivery

  • Drive technical execution and delivery of Foundation and Evolved POC phases.
  • Support build configuration, validation, and user acceptance activities.
  • Conduct performance benchmarking and technical assessments.
  • Contribute to POC evaluation reports, including:
    • Findings and recommendations
    • Go/No-Go assessments
    • Production scale-up strategies
  • Document technical decisions, risks, assumptions, and remediation plans.

Client Engagement & Stakeholder Management

  • Serve as the primary technical point of contact for architecture and data engineering topics.
  • Collaborate closely with customer IT, AI/ML CoE, Data Engineering, Clinical Data Management, and Study Setup teams.
  • Facilitate technical workshops, architecture reviews, and iterative solution validation sessions.
  • Communicate progress, risks, and recommendations to technical and business stakeholders.
  • Participate in iterative review cycles with Study Setup teams and document findings, remediation actions, and technical decisions.

Required Qualifications

  • Bachelor's degree in Computer Science, Information Systems, Data Engineering, Life Sciences, or a related discipline.
  • Proven experience as an AI Architect, Solution Architect, Data Architect, or Data Engineering Lead.
  • Strong experience designing enterprise-scale AI and data-driven solutions.
  • Hands-on experience with Knowledge Graph technologies, metadata management, and semantic data models.
  • Experience building and supporting data ingestion, transformation, and integration pipelines.
  • Strong understanding of REST APIs, enterprise integration patterns, and system interoperability.
  • Experience with Large Language Models (LLMs), Generative AI architectures, and AI-enabled workflow automation.
  • Experience with browser automation frameworks such as Playwright or similar technologies.
  • Strong troubleshooting and problem-solving skills across complex technology ecosystems.
  • Excellent communication, stakeholder management, and documentation skills.

Preferred Qualifications

  • Experience within the Life Sciences, Biotechnology, or Pharmaceutical industry.
  • Knowledge of Clinical Data Management (CDM) processes and study setup workflows.
  • Experience working with Veeva CDMS, Veeva CTMS, or similar clinical platforms.
  • Familiarity with clinical metadata standards and study design methodologies.
  • Experience collaborating with AI/ML Centers of Excellence and enterprise data teams.
  • Understanding of GxP, regulatory compliance, and clinical data governance requirements.
  • Experience supporting Proof of Concept initiatives through production deployment.

Salary and Other Compensation

The annual salary for this position is between $[120,000] - $[150,000], depending on experience, qualifications, geographic location, and other factors permitted by law.

This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of the applicable plan.

Benefits include:

  • Medical, Dental, Vision, and Life Insurance
  • Paid Holidays and Paid Time Off
  • 401(k) Plan and Contributions
  • Employee Assistance Program
  • Disability and Leave Programs
  • Learning and Development Opportunities
  • Additional Benefits Available Based on Role and Location

About Cognizant

Cognizant is one of the world's leading professional services companies, transforming clients' business, operating, and technology models for the digital era. Our industry-based, consultative approach helps clients envision, build, and run more innovative and efficient businesses. We help some of the world's most recognized organizations modernize technology, reimagine processes, and transform experiences to stay ahead in a rapidly changing world.


Equal Opportunity Employer

Cognizant is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, disability, age, protected veteran status, or any other characteristic protected by applicable law.


À propos de Cognizant 
Cognizant (NASDAQ : CTSH) est un AI Builder et une entreprise de services numériques (ESN) élaborant des solutions complètes d’IA maximisant les investissements pour des résultats concrets. Sa profonde expertise des métiers, des processus et des technologies lui permet d’intégrer dans les systèmes technologiques le contexte unique de chaque organisation de l’ingénierie à la production à l’échelle. Son objectif : améliorer l’efficacité des équipes, créer de la valeur et permettre aux grandes entreprises de rester performantes dans un monde qui évolue rapidement. Pour en savoir plus : cognizant.ai ou @cognizant. 

Renseignments suppplémentaires sur l'emploi
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