{"id":3728125,"url":"https://alion.io/job/capgemini-ai-ml-developer","title":"AI ML Developer","company":{"id":145,"name":"Capgemini","domain":"capgemini.com","url":"https://alion.io/company/capgemini","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":79,"open_postings":1755,"ghost_share":0.004,"stale_share":0.813,"repost_share":0.018,"time_to_fill_p50_days":30,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Aguascalientes, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Airflow","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Docker","optional":true},{"name":"Feature Store","optional":true},{"name":"Gemini","optional":true},{"name":"Google AI Studio","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Google Cloud Run","optional":true},{"name":"Google GKE","optional":true},{"name":"Hybrid Search","optional":true},{"name":"JAX","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"NLP","optional":true},{"name":"Platform Engineering","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Semantic Search","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true},{"name":"Transformers","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-11-03T18:42:06Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-07T01:35:21Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"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.\nYour Role\nAs 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.\nYou 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.\nYou will contribute to the development of production-grade AI solutions spanning predictive analytics, intelligent automation, conversational AI, and next-generation Generative AI applications.\nResponsibilities & Scope\nMachine Learning Engineering\nDesign, develop, test, deploy, and maintain Machine Learning models and AI-powered applications.\nBuild scalable feature engineering, training, validation, and inference pipelines.\nDevelop production-ready model services, APIs, and AI-enabled application components.\nImplement model monitoring, performance tracking, and continuous improvement processes.\nEnsure reliability, scalability, and maintainability of ML solutions in production environments.\nGenerative AI & LLM Solutions\nDesign and implement enterprise Generative AI solutions leveraging Large Language Models (LLMs).\nDevelop prompt engineering strategies and model optimization approaches.\nBuild Retrieval-Augmented Generation (RAG) architectures for knowledge retrieval and intelligent search.\nImplement vector search solutions leveraging embeddings and vector databases.\nEvaluate and optimize AI solutions using established AI evaluation methodologies and benchmarks.\nSupport customization and fine-tuning of foundation models.\nAgentic AI & Multi-Agent Systems\nDesign and develop Agentic AI solutions utilizing:\nLangChain\nLangGraph\nMulti-Agent Architectures\nAgent Orchestration Frameworks\nTool Calling\nWorkflow Automation\nBuild intelligent autonomous workflows that leverage reasoning, planning, memory, and external tools.\nImplement secure and scalable AI agent frameworks aligned with enterprise requirements.\nData Engineering & AI Pipelines\nDevelop robust data preparation and feature engineering pipelines.\nDesign end-to-end ML workflows supporting:\nData Ingestion\nFeature Creation\nModel Training\nEvaluation\nDeployment\nMonitoring\nCollaborate with data engineering teams to support scalable AI and analytics platforms.\nEnsure data quality, lineage, and reproducibility across AI initiatives.\nCloud AI & MLOps\nDeploy and manage AI workloads within Google Cloud Platform or similar cloud environments.\nImplement MLOps practices and automation pipelines.\nDesign CI/CD workflows supporting machine learning lifecycle management.\nDevelop scalable model serving architectures and inference endpoints.\nSupport containerized and cloud-native AI deployments.\nCollaboration & Innovation\nPartner with Product Owners and Business Stakeholders to translate business objectives into measurable AI use cases.\nDefine acceptance criteria, success metrics, and performance objectives for AI solutions.\nParticipate in architecture discussions, technical design reviews, and innovation initiatives.\nMentor team members and contribute to AI engineering best practices.\nYour Profile\nAI innovator - Passionate about Machine Learning, Generative AI, and emerging technologies.\nHands-on builder - Enjoys designing and developing production-grade AI solutions.\nProblem solver - Able to simplify complex challenges through data-driven approaches.\nCloud-native engineer - Comfortable building scalable solutions in modern cloud environments.\nCollaborative professional - Thrives in cross-functional teams and agile environments.\nContinuous learner - Stays current with advancements in AI, ML, MLOps, and GenAI technologies.\nQualifications & Preferred Skills\nRequired Skills\nMachine Learning & Deep Learning\nStrong expertise in Machine Learning and Deep Learning techniques.\nExperience building production-grade predictive models and AI solutions.\nSolid understanding of:\nSupervised Learning\nUnsupervised Learning\nDeep Learning\nNatural Language Processing (NLP)\nTransformers Architecture\nGenerative AI & RAG\nHands-on experience with:\nLarge Language Models (LLMs)\nRetrieval-Augmented Generation (RAG)\nEmbeddings\nVector Databases\nSemantic Search\nHybrid Search\nPrompt Engineering\nExperience implementing end-to-end RAG pipelines.\nKnowledge of AI evaluation methodologies and model benchmarking.\nAgentic AI\nExperience designing and implementing Agentic AI systems.\nHands-on experience with:\nLangChain\nLangGraph\nMulti-Agent Architectures\nAgent Orchestration Frameworks\nTool Integration\nWorkflow Automation\nProgramming & Frameworks\nAdvanced proficiency in:\nPython\nSQL\nHands-on experience with:\nTensorFlow\nPyTorch\nJAX\nStrong software engineering and API development capabilities.\nCloud & Deployment\nExperience deploying AI/ML solutions on:\nGoogle Cloud Platform (preferred)\nAzure\nAWS\nExperience with production deployment, scaling, and operational support of AI systems.\nPreferred Skills\nGoogle Cloud & Vertex AI\nExperience leveraging:\nVertex AI\nVertex AI Pipelines\nVertex AI Workbench\nModel Registry\nFeature Store\nVertex AI Studio\nExperience customizing and deploying Gemini-based solutions.\nData Engineering & Analytics\nBigQuery\nBigQuery ML\nCloud Storage\nDataflow\nApache Beam\nDataproc\nEnterprise Data Pipelines\nMLOps & Platform Engineering\nKubeflow\nCloud Composer (Airflow)\nCI/CD for ML\nContainerization (Docker)\nKubernetes (GKE)\nCloud Run\nAutomated Model Deployment\nModel Operations\nModel Monitoring\nFeature Drift Detection\nData Drift Analysis\nLatency Monitoring\nModel Performance Optimization\nAI Governance and Responsible AI Practices\nWhat You'll Love\nOpportunity to work on cutting-edge AI, Machine Learning, and Generative AI initiatives.\nExposure to enterprise-scale AI transformation programs and emerging technologies.\nAccess to Google Cloud AI, Vertex AI, Gemini, and advanced ML platforms.\nCollaboration with global Data, AI, Analytics, and Cloud experts.\nContinuous learning through certifications, technical academies, mentoring, and innovation programs.\nCareer growth opportunities within Capgemini's Global Data & AI practice.\nNeed to Know\nCareer Development in Spanish & English: Access to AI, Machine Learning, Cloud, and Data Engineering certifications, mentorship programs, technical academies, and continuous learning opportunities.\nCommunity Engagement: Participate in AI communities, innovation labs, hackathons, knowledge-sharing forums, technical events, and volunteering initiatives across Capgemini.\nAbout Capgemini\nCapgemini is a global business and technology transformation partner, helping organizations accelerate their dual transition to a digital and sustainable world while creating tangible impact for enterprises and society. It is a responsible and diverse organization with more than 340,000 team members in over 50 countries.\nWith a strong heritage of more than 55 years, Capgemini helps clients unlock the value of technology through end-to-end services and solutions, leveraging capabilities in strategy and design, engineering, AI, generative AI, cloud, data, and digital transformation.\nAt Capgemini Mexico, we are committed to fostering a diverse and inclusive workplace where everyone has equal opportunities. 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