AI ML Developer

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. Your Role As a Senior Machine Learning Engineer at Capgemini , you will be responsible for designing, developing, deploying, and maintaining advanced Artificial Intelligence and Machine Learning solutions that drive business innovation and operational excellence. You will work with multidisciplinary teams including Product Owners, Data Scientists, Data Engineers, Architects, and Business Stakeholders to transform complex business challenges into scalable AI-powered products. This role requires expertise in machine learning engineering, Generative AI, Agentic AI systems, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), MLOps, and cloud-native AI platforms. You will contribute to the development of production-grade AI solutions spanning predictive analytics, intelligent automation, conversational AI, and next-generation Generative AI applications. Responsibilities & Scope Machine Learning Engineering • Design, develop, test, deploy, and maintain Machine Learning models and AI-powered applications. • Build scalable feature engineering, training, validation, and inference pipelines. • Develop production-ready model services, APIs, and AI-enabled application components. • Implement model monitoring, performance tracking, and continuous improvement processes. • Ensure reliability, scalability, and maintainability of ML solutions in production environments. Generative AI & LLM Solutions • Design and implement enterprise Generative AI solutions leveraging Large Language Models (LLMs). • Develop prompt engineering strategies and model optimization approaches. • Build Retrieval-Augmented Generation (RAG) architectures for knowledge retrieval and intelligent search. • Implement vector search solutions leveraging embeddings and vector databases. • Evaluate and optimize AI solutions using established AI evaluation methodologies and benchmarks. • Support customization and fine-tuning of foundation models. Agentic AI & Multi-Agent Systems • Design and develop Agentic AI solutions utilizing: • LangChain • LangGraph • Multi-Agent Architectures • Agent Orchestration Frameworks • Tool Calling • Workflow Automation • Build intelligent autonomous workflows that leverage reasoning, planning, memory, and external tools. • Implement secure and scalable AI agent frameworks aligned with enterprise requirements. Data Engineering & AI Pipelines • Develop robust data preparation and feature engineering pipelines. • Design end-to-end ML workflows supporting: • Data Ingestion • Feature Creation • Model Training • Evaluation • Deployment • Monitoring • Collaborate with data engineering teams to support scalable AI and analytics platforms. • Ensure data quality, lineage, and reproducibility across AI initiatives. Cloud AI & MLOps • Deploy and manage AI workloads within Google Cloud Platform or similar cloud environments. • Implement MLOps practices and automation pipelines. • Design CI/CD workflows supporting machine learning lifecycle management. • Develop scalable model serving architectures and inference endpoints. • Support containerized and cloud-native AI deployments. Collaboration & Innovation • Partner with Product Owners and Business Stakeholders to translate business objectives into measurable AI use cases. • Define acceptance criteria, success metrics, and performance objectives for AI solutions. • Participate in architecture discussions, technical design reviews, and innovation initiatives. • Mentor team members and contribute to AI engineering best practices. Your Profile • AI innovator – Passionate about Machine Learning, Generative AI, and emerging technologies. • Hands-on builder – Enjoys designing and developing production-grade AI solutions. • Problem solver – Able to simplify complex challenges through data-driven approaches. • Cloud-native engineer – Comfortable building scalable solutions in modern cloud environments. • Collaborative professional – Thrives in cross-functional teams and agile environments. • Continuous learner – Stays current with advancements in AI, ML, MLOps, and GenAI technologies. Qualifications & Preferred Skills Required Skills Machine Learning & Deep Learning • Strong expertise in Machine Learning and Deep Learning techniques. • Experience building production-grade predictive models and AI solutions. • Solid understanding of: • Supervised Learning • Unsupervised Learning • Deep Learning • Natural Language Processing (NLP) • Transformers Architecture Generative AI & RAG • Hands-on experience with: • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • Embeddings • Vector Databases • Semantic Search • Hybrid Search • Prompt Engineering • Experience implementing end-to-end RAG pipelines. • Knowledge of AI evaluation methodologies and model benchmarking. Agentic AI • Experience designing and implementing Agentic AI systems. • Hands-on experience with: • LangChain • LangGraph • Multi-Agent Architectures • Agent Orchestration Frameworks • Tool Integration • Workflow Automation Programming & Frameworks • Advanced proficiency in: • Python • SQL • Hands-on experience with: • TensorFlow • PyTorch • JAX • Strong software engineering and API development capabilities. Cloud & Deployment • Experience deploying AI/ML solutions on: • Google Cloud Platform (preferred) • Azure • AWS • Experience with production deployment, scaling, and operational support of AI systems. Preferred Skills Google Cloud & Vertex AI • Experience leveraging: • Vertex AI • Vertex AI Pipelines • Vertex AI Workbench • Model Registry • Feature Store