{"id":3072168,"url":"https://alion.io/job/mercedes-benz-data-and-ai-engineer","title":"Data and AI Engineer","company":{"id":136,"name":"Mercedes-Benz","domain":"mercedes-benz.com","url":"https://alion.io/company/mercedes-benz","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"A","score":98,"open_postings":42,"ghost_share":0,"stale_share":0.071,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"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":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Diffusion Models","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"AutoGPT","optional":true},{"name":"FAISS","optional":true},{"name":"Interpretability","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Pinecone","optional":true},{"name":"Weaviate","optional":true}],"status":"closed","first_seen_at":"2026-10-22T09:44:01Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-27T07:37:05Z","board_verified":false,"closed_at":"2026-10-27T07:37:05Z","days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"Aufgaben\n About MBRDI:\nMercedes-Benz Research and Development India (MBRDI), headquartered in Bengaluru with a satellite office in Pune, is the largest R&D center for Mercedes-Benz Group AG outside of Germany. Our mission is to drive innovation and excellence in automotive engineering, digitalization, and sustainable mobility solutions, shaping the future of mobility.\nWe are seeking a motivated and curious AI Engineer with a strong interest in Generative AI (GenAI) and Data Science to join our Applied AI team. In this role, you will contribute to the development and deployment of cutting-edge AI solutions leveraging Large Language Models (LLMs) and other GenAI technologies. This is an excellent opportunity for early-career professionals to gain hands-on experience in applying AI to real-world challenges across business and technical domains.\nKey Responsibilities\nModel Development & ExperimentationWork on training, fine-tuning, and evaluating generative AI models (LLMs, diffusion models, multimodal systems).\nExperiment with prompt engineering, RAG (Retrieval-Augmented Generation), and agent-based architectures.\nBenchmark model performance and optimize for accuracy, efficiency, and scalability.\n\nPrototyping & DeploymentBuild proof-of-concept applications showcasing GenAI capabilities for business use cases.\nSupport integration of GenAI models into enterprise workflows, APIs, or platforms.\nCollaborate with MLOps engineers to ensure models are production-ready.\n\nResearch & InnovationStay updated with the latest advancements in GenAI, NLP, and multimodal AI.\nExplore new architectures, frameworks, and evaluation methods.\nContribute to knowledge sharing within the team and broader organization.\n\nCollaboration & Stakeholder EngagementWork closely with cross-functional teams (product, engineering, business) to understand problem statements and co-create solutions.\nTranslate research findings into actionable insights for business applications.\nPresent results and demos to both technical and non-technical stakeholders.\n\nKey Responsibilities\nModel Development & ExperimentationWork on training, fine-tuning, and evaluating generative AI models (LLMs, diffusion models, multimodal systems).\nExperiment with prompt engineering, RAG (Retrieval-Augmented Generation), and agent-based architectures.\nBenchmark model performance and optimize for accuracy, efficiency, and scalability.\n\nPrototyping & DeploymentBuild proof-of-concept applications showcasing GenAI capabilities for business use cases.\nSupport integration of GenAI models into enterprise workflows, APIs, or platforms.\nCollaborate with MLOps engineers to ensure models are production-ready.\n\nResearch & InnovationStay updated with the latest advancements in GenAI, NLP, and multimodal AI.\nExplore new architectures, frameworks, and evaluation methods.\nContribute to knowledge sharing within the team and broader organization.\n\nCollaboration & Stakeholder Engagement\nWork closely with cross-functional teams (product, engineering, business) to understand problem statements and co-create solutions.\nTranslate research findings into actionable insights for business applications.\nPresent results and demos to both technical and non-technical stakeholders.\n\n Qualifikationen\n Required Qualifications\nBachelor’s/Master’s in Computer Science, Data Science, AI/ML, Applied Mathematics, or a related field.\nStrong foundations in Machine Learning, Deep Learning, and Natural Language Processing (NLP).\nProficiency in Python and relevant ML/AI libraries (PyTorch, TensorFlow, Hugging Face, LangChain).\nBasic knowledge of transformer architectures and experience working with pre-trained models.\nFamiliarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes) is a plus.\nStrong problem-solving, analytical, and communication skills.\nPreferred Skills (Good to Have)\nExperience with vector databases (Pinecone, FAISS, Weaviate) for RAG pipelines.\nExposure to agent frameworks (LangChain Agents, LlamaIndex, AutoGPT, etc.).\nUnderstanding of responsible AI practices (bias, interpretability, security).\nContributions to open-source AI/ML projects or participation in hackathons.","description_format":"text","description_chars":4175,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Sports Organizations","Automotive 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