Capgemini Invent - Contextual Systems Engineer Senior Consultant

About the role

At Capgemini Invent, our Data Driven Transformation (DDT) team makes AI work at scale by combining data science and engineering with business consulting. We build context-driven analytics and GenAI/agentic systems that reflect how decisions and work actually happen—grounded in semantic layers, strong data foundations, and governance embedded in decision flows. With a builder-advisor (“forward deployed”) mindset, DDT delivers production-ready impact across operations and customer domains, redefining what enterprise AI advisory looks like in practice.

Responsibilities

• Designing and implementing semantic layer architectures end-to-end, including LookML models, dbt metrics, and ontologies
• Building knowledge graph schemas and implementing GraphRAG patterns for contextual AI retrieval
• Defining context contracts, including versioned semantic objects, governed datasets, and metric definitions
• Leading technical workshops to extract and encode enterprise knowledge into machine-readable forms
• Evaluating and configuring knowledge platform tooling, including graph databases, vector stores, and embedding pipelines
• Partnering with AI engineering teams to integrate context layers into agentic and AI workflows

Requirements

• Strong AI literacy, including core AI and generative AI concepts, and understanding how these apply to enterprise transformation
• Experience working in AI-augmented ways of working to enhance research, analysis, and solution development
• Collaboration across strategy, technology, data, and design to enable AI-driven solutions, with a solid understanding of responsible AI principles
• Curiosity and continuous learning mindset around emerging AI capabilities and their practical application in client environments
• Experience in data engineering, data architecture, or knowledge management
• Hands-on expertise with semantic layer tooling such as LookML, dbt, or equivalent technologies
• Experience designing and working with knowledge graphs (Neo4j preferred)
• Familiarity with modern LLM integration patterns, including RAG, embedding pipelines, and vector stores
• Strong ability to translate business semantics into formal, well-governed technical specifications

Skills

• Experience working across cloud data platforms such as BigQuery or Snowflake
• Exposure to governance and catalog tooling (e.g., Collibra, Atlan)
• Interest in applied AI, agentic architectures, and enterprise-scale contextual systems

Benefits

The base compensation range for this role in the posted location is: $105,600 - $199,480. 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.

Pay

The base compensation range for this role in the posted location is: $105,600 - $199,480.

Schedule

Not specified.