Responsibilities

• Build and maintain scalable batch and streaming data pipelines for ingestion transformation and curation
• Design and optimize data models feature stores and storage patterns for AIML workloads
• Implement DataOps and MLOps automation CICD data validation model deployment monitoring drift detection
• Design build and optimize agentic AI systems and LLMpowered applications RAG pipelines agent orchestration
• Develop and integrate AI services into secure productiongrade environments
• Design and implement AI systems architecture best practices and standards across the organization
• Build scalable resilient cloud and onpremise systems for hosting AILLM applications
• Provide infrastructure design optimization and monitoring support
• Translate business requirements into technical solutions through crossfunctional collaboration
• Ensure code quality system reliability scalability and observability

Expectations Skillset Required

• Advanced Python proficiency with strong software engineering fundamentals version control testing code reviews
• Proficiency in SQL and distributed computing Spark distributed systems
• Deep experience with data modeling and feature engineering across the ML lifecycle
• Handson experience building LLM applications RAG pipelines and agentic AI frameworks LangChainLangGraph preferred
• Expertise designing distributed systems at enterprise scale
• Strong CICD and DevOps knowledge GitHubGitHub Actions Docker Kubernetes
• Experience with event streaming platforms Kafka
• Demonstrated ability to design and deploy hybrid cloud and onpremise systems AWS Azure
• Excellent collaboration and communication skills across engineering research and product teams
• Selfdirected problemsolving mindset and ability to navigate enterprise complexity
• Masters degree in Computer Science Software Engineering or related field with 5 years in datasoftwareMLAI engineering preferred
• UI development expertise with React and JavaScript a plus'

• Advanced Python proficiency with strong software engineering fundamentals version control testing code reviews
• Proficiency in SQL and distributed computing Spark distributed systems
• Deep experience with data modeling and feature engineering across the ML lifecycle
• Handson experience building LLM applications RAG pipelines and agentic AI frameworks LangChainLangGraph preferred
• Expertise designing distributed systems at enterprise scale
• Strong CICD and DevOps knowledge GitHubGitHub Actions Docker Kubernetes
• Experience with event streaming platforms Kafka
• Demonstrated ability to design and deploy hybrid cloud and onpremise systems AWS Azure
• Excellent collaboration and communication skills across engineering research and product teams
• Selfdirected problemsolving mindset and ability to navigate enterprise complexity
• Masters degree in Computer Science Software Engineering or related field with 5 years in datasoftwareMLAI engineering preferred
• UI development expertise with React and JavaScript a plus'

The base compensation range for this role in the posted location is 83,000 to 104,000

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave

• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility

Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini's discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.

Disclaimers