Capgemini is seeking a Senior AI/ML Engineer to help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world by architecting and scaling AI-first applications that leverage the latest in LLM, orchestration, and AWS-native services.
Requirements
- Total 4 to 5 + years of experience in machine learning, with a focus on NLP and Generative AI.
- Strong experience building and deploying intent detection, text classification, sequence tagging, and entity recognition models.
- Proficient in LangChain, LangGraph, vector databases (e.g., FAISS, Pinecone), and orchestration of LLM workflows.
- Deep knowledge of AWS Bedrock, Amazon SageMaker, Lambda, DynamoDB, Step Functions, etc.
- Experience working with open-source LLMs (LLaMA, Mistral, Falcon) or commercial APIs (Claude, GPT-4, etc.).
- Proficient in Python, with a solid grasp of ML frameworks such as PyTorch, HuggingFace Transformers, scikit-learn.
- Strong understanding of MLOps practices including model versioning, CI/CD for ML, monitoring, and auto-scaling.
Responsibilities
- Design, develop, and deploy intent classification and intent detection models using LLMs and traditional NLP methods.
- Build and optimize Natural Language Generation (NLG) pipelines for chatbot responses, summarization, content creation, or knowledge grounding.
- Architect and implement LangChain and LangGraph based applications for LLM-driven workflows (e.g., autonomous agents, RAG systems).
- Develop scalable machine learning pipelines using the AWS tech stack (e.g., Sagemaker, Lambda, Bedrock, Step Functions, DynamoDB, Athena).
- Integrate and fine-tune foundation models via AWS Bedrock, including Amazon Titan, Anthropic Claude, or Meta Llama.
- Collaborate closely with product managers, ML researchers, and backend engineers to translate business requirements into robust AI solutions.
- Lead experimentation efforts, conduct A/B testing, and ensure continuous evaluation of deployed ML models.
Other
- Bachelor's or Master's in Computer Science, Data Science, or a related field.
- Flexible work arrangements, including remote work options.
- 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.
