Data and AI Engineer

About MBRDI

Mercedes-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.

We 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.

Model Development & Experimentation

• Work on training, fine-tuning, and evaluating generative AI models (LLMs, diffusion models, multimodal systems).
• Experiment with prompt engineering, RAG (Retrieval-Augmented Generation), and agent-based architectures.
• Benchmark model performance and optimize for accuracy, efficiency, and scalability.

Prototyping & Deployment

• Build proof-of-concept applications showcasing GenAI capabilities for business use cases.
• Support integration of GenAI models into enterprise workflows, APIs, or platforms.
• Collaborate with MLOps engineers to ensure models are production-ready.

Research & Innovation

• Stay updated with the latest advancements in GenAI, NLP, and multimodal AI.
• Explore new architectures, frameworks, and evaluation methods.
• Contribute to knowledge sharing within the team and broader organization.

Collaboration & Stakeholder Engagement

• Work closely with cross-functional teams (product, engineering, business) to understand problem statements and co-create solutions.
• Translate research findings into actionable insights for business applications.
• Present results and demos to both technical and non-technical stakeholders.

Key Responsibilities

Model Development & Experimentation

• Work on training, fine-tuning, and evaluating generative AI models (LLMs, diffusion models, multimodal systems).
• Experiment with prompt engineering, RAG (Retrieval-Augmented Generation), and agent-based architectures.
• Benchmark model performance and optimize for accuracy, efficiency, and scalability.

Prototyping & Deployment

• Build proof-of-concept applications showcasing GenAI capabilities for business use cases.
• Support integration of GenAI models into enterprise workflows, APIs, or platforms.
• Collaborate with MLOps engineers to ensure models are production-ready.

Research & Innovation

• Stay updated with the latest advancements in GenAI, NLP, and multimodal AI.
• Explore new architectures, frameworks, and evaluation methods.
• Contribute to knowledge sharing within the team and broader organization.

Collaboration & Stakeholder Engagement

• Work closely with cross-functional teams (product, engineering, business) to understand problem statements and co-create solutions.
• Translate research findings into actionable insights for business applications.
• Present results and demos to both technical and non-technical stakeholders.

Required Qualifications

• Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, Applied Mathematics, or a related field.
• Strong foundations in Machine Learning, Deep Learning, and Natural Language Processing (NLP).
• Proficiency in Python and relevant ML/AI libraries (PyTorch, TensorFlow, Hugging Face, LangChain).
• Basic knowledge of transformer architectures and experience working with pre-trained models.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes) is a plus.
• Strong problem-solving, analytical, and communication skills.

Preferred Skills (Good to Have)

• Experience with vector databases (Pinecone, FAISS, Weaviate) for RAG pipelines.
• Exposure to agent frameworks (LangChain Agents, LlamaIndex, AutoGPT, etc.).
• Understanding of responsible AI practices (bias, interpretability, security).
• Contributions to open-source AI/ML projects or participation in hackathons.