Senior GenAI Engineer​/Lead

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 bya 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.

• We are seeking a GenAI & AI/ML Framework Specialist to design, build, and scale next-generation artificial intelligence solutions.
• You will develop advanced machine learning algorithms, optimize open-source frameworks, and implement Generative AI architectures into enterprise applications.

• GenAI & Model Development
  Design and deploy Generative AI solutions using Large Language Models (LLMs) and diffusion models.
  Implement optimization techniques including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
  Develop predictive models by selecting, training, and tuning traditional machine learning and deep learning algorithms.
• Framework & Pipeline Engineering
  Build scalable pipelines for data preprocessing, feature engineering, and model training.
  Optimize AI frameworks to improve inference speed, reduce latency, and lower compute costs.
  Integrate AI models seamlessly into production software architectures and enterprise workflows.
• Technical Skills Required
  AI/ML Frameworks & Libraries
  Core Frameworks:
  Deep expertise in PyTorch, Tensor Flow, or JAX.
  GenAI Ecosystem:
  Hands‑on experience with Hugging Face, Lang Chain, Llama Index, and vLLM.
  Core
  
  Languages:
  
  Mastery of Python for performance tuning.
• Algorithms & Math
  Machine Learning:
  Deep understanding of regression, clustering, decision trees, and ensemble methods.
  Deep Learning:
  Strong grasp of Transformers, CNNs, RNNs, and reinforcement learning (RLHF).
  Data
  
  Infrastructure:
  
  Experience with Vector Databases (ChromaDB, Pinecone, Milvus) and SQL/No
  
  SQL.
• MLOps & Infrastructure
  Deployment:
  
  Experience with ML flow, Kubeflow, or Triton Inference Server.
  Cloud & Compute:
  Proficiency with AWS (Sage Maker), Azure (Azure AI), or GCP (Vertex AI), alongside GPU acceleration (CUDA).

• Experience:
  
  8 years in data science or AI engineering, with 2+ years dedicated to Generative AI.
• Education:
  
  Master's in Computer Science, Data Science, Mathematics, or a related quantitative field.

The base compensation range for this role in the posted location is: 79,000 to 105,000.

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…