Capgemini
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Job Description We are seeking a candidate with 8+ years of experience in Data Engineering and Machine Learning, with strong expertise in Azure Data Platform and hands‑on experience in LLM‑based solutions and Retrieval‑Augmented Generation (RAG) architectures. The ideal candidate will design, implement, and optimize data and AI solutions leveraging Microsoft Azure and modern AI frameworks.
Responsibilities
Design and develop data ingestion pipelines from source systems using Azure Databricks and Azure Data Factory into the Azure Analytics Platform.
Build and optimize ETL/ELT pipelines for structured and unstructured data.
Develop and deploy LLM‑powered applications, including RAG‑based solutions for enterprise use cases.
Implement vector databases and embedding‑based retrieval systems to support RAG workflows.
Integrate LLMs with Azure services (e.g., Azure OpenAI, Cognitive Search) for intelligent data processing and insights.
Apply Python, PySpark and modern ML frameworks to build scalable AI solutions.
Provide technical design and coding guidance to the team to achieve project deliverables.
Ensure CI/CD integration using Azure DevOps for ML and data pipelines.
Collaborate with business stakeholders to gather requirements and translate them into technical solutions.
Stay current with AI/ML trends, including Generative AI, LLM fine‑tuning, and prompt engineering.
Skills & Expertise
ETL & ELT, Data Warehousing, SQL, Relational Databases
Python, PySpark, and ML frameworks (e.g., Hugging Face, LangChain)
LLM development and deployment (Azure OpenAI, or similar)
Prompt Engineering, LLM Fine‑tuning, and Model Evaluation
Experience with Cloud Platforms (Azure is a must)
Preferred Qualifications
Experience with MLOps for LLMs, including model lifecycle management and monitoring.
Knowledge of semantic search, embedding optimization, and knowledge graph integration.
Familiarity with distributed systems and scalable AI architectures.
Understanding of data governance, security, and compliance in AI/ML solutions.
Strong problem‑solving skills and ability to architect end‑to‑end AI solutions.
Life at Capgemini
Flexible work
Healthcare including dental, vision, mental health, and well‑being programs
Financial well‑being programs such as 401(k) and Employee Share Ownership Plan
Paid time off and paid holidays
Paid parental leave
Family building benefits like adoption assistance, surrogacy, and cryopreservation
Social well‑being benefits like subsidized back‑up child/elder care and tutoring
Mentoring, coaching and learning programs
Employee Resource Groups
Disaster Relief
Disclaimer Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
#J-18808-Ljbffr
Job Description We are seeking a candidate with 8+ years of experience in Data Engineering and Machine Learning, with strong expertise in Azure Data Platform and hands‑on experience in LLM‑based solutions and Retrieval‑Augmented Generation (RAG) architectures. The ideal candidate will design, implement, and optimize data and AI solutions leveraging Microsoft Azure and modern AI frameworks.
Responsibilities
Design and develop data ingestion pipelines from source systems using Azure Databricks and Azure Data Factory into the Azure Analytics Platform.
Build and optimize ETL/ELT pipelines for structured and unstructured data.
Develop and deploy LLM‑powered applications, including RAG‑based solutions for enterprise use cases.
Implement vector databases and embedding‑based retrieval systems to support RAG workflows.
Integrate LLMs with Azure services (e.g., Azure OpenAI, Cognitive Search) for intelligent data processing and insights.
Apply Python, PySpark and modern ML frameworks to build scalable AI solutions.
Provide technical design and coding guidance to the team to achieve project deliverables.
Ensure CI/CD integration using Azure DevOps for ML and data pipelines.
Collaborate with business stakeholders to gather requirements and translate them into technical solutions.
Stay current with AI/ML trends, including Generative AI, LLM fine‑tuning, and prompt engineering.
Skills & Expertise
ETL & ELT, Data Warehousing, SQL, Relational Databases
Python, PySpark, and ML frameworks (e.g., Hugging Face, LangChain)
LLM development and deployment (Azure OpenAI, or similar)
Prompt Engineering, LLM Fine‑tuning, and Model Evaluation
Experience with Cloud Platforms (Azure is a must)
Preferred Qualifications
Experience with MLOps for LLMs, including model lifecycle management and monitoring.
Knowledge of semantic search, embedding optimization, and knowledge graph integration.
Familiarity with distributed systems and scalable AI architectures.
Understanding of data governance, security, and compliance in AI/ML solutions.
Strong problem‑solving skills and ability to architect end‑to‑end AI solutions.
Life at Capgemini
Flexible work
Healthcare including dental, vision, mental health, and well‑being programs
Financial well‑being programs such as 401(k) and Employee Share Ownership Plan
Paid time off and paid holidays
Paid parental leave
Family building benefits like adoption assistance, surrogacy, and cryopreservation
Social well‑being benefits like subsidized back‑up child/elder care and tutoring
Mentoring, coaching and learning programs
Employee Resource Groups
Disaster Relief
Disclaimer Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
#J-18808-Ljbffr