MBRDI
Technology
AppliedDataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Applied Data Scientist at MBRDI. Skills: AI, GenAI, LLMs, Multi-Agentic Systems. Explore and build with LLMs. Experiment with new LLMs”
What You'll Achieve.
Convert experimental agents into enterprise-ready pipelines; Scale prototypes into real applications
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
5-7 years of experience in data science / applied ML / AI engineering, Python development background, Experience with Git/GitHub workflows, Practical exposure to LLMs, embeddings, and RAG pipelines, Hands-on experience with orchestrators (LangChain, LlamaIndex, CrewAI), Experience building AI Agents and/or multi-agent systems, Knowledge of cloud platforms, Understanding of scalable and enterprise application development
Nice to Have
MCP (Model Context Protocol) experimentation, Databricks, Delta Lake, or Spark exposure, Contributions to open-source AI tools or repos, Automation experience with n8n or agent frameworks, Experience with structured + unstructured data pipelines
What You'll Do.
Explore and build with LLMs
Experiment with new LLMs
Try new prompting patterns
Try fine-tuning methods
Try retrieval strategies
Turn ideas into prototypes
Work with orchestrator frameworks
Build multi-step workflows
Build context managers
Build tool-based actions
Try different agent architectures
Try communication patterns
Design multi-agentic systems
Build agents that collaborate
Build agents that debate
Build agents that plan
Build agents that execute tasks autonomously
Experiment with A2A interaction workflows
Convert experimental agents into enterprise-ready pipelines
Learn to scale prototypes into real applications
Use Azure services to deploy AI workloads
Work with microservices
Work with cloud best practices
Participate in internal hackathons
Participate in AI/GenAI forums
Bring fresh ideas to the team
Bring PoCs to the team
Bring innovative techniques to the team
How You'll Work.
Team & Collaboration
Cross-functional teams
Process & Methodology
CI/CD, Microservices
Full Job Description
Applied Data Scientist Job Description: Applied Data Scientist – AI/GenAI Experience: 5–7 Years Location: Bangalore Employment Type: Full-Time 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. About the Role We are looking for AI Enthusiasts —applied data scientists who thrive on learning, experimenting, and building the future of AI. This role is perfect for someone who is curious by nature , loves exploring new models and frameworks, and wants to grow into an expert in LLMs, AI Agents, and Multi-Agentic Systems . If you are someone who tinkers with tools even after work hours, reads about the latest breakthroughs, or enjoys turning ideas into prototypes— you will feel at home here . Key Responsibilities: Explore and Build with LLMs Experiment with new LLMs (GPT, Llama, Claude, Mistral, etc.). Try out new prompting patterns, fine-tuning methods, and retrieval strategies. Turn ideas into prototypes— quickly and creatively . Play, Learn, and Create with Orchestrator Frameworks Work with LangChain, LlamaIndex, CrewAI, or anything new that emerges. Build multi-step workflows, context managers, memory modules, and tool-based actions. Try different agent architectures and communication patterns. Design Multi-Agentic Systems Build agents that collaborate, debate, plan, and execute tasks autonomously. Experiment with A2A (Agent-to-Agent) interaction workflows. Convert experimental agents into enterprise-ready pipelines. Grow into Enterprise-Scale AI Learn how to scale your prototypes into real applications. Use Azure services—Azure OpenAI, App Service, Functions, AKS, ADLS—to deploy AI workloads. Work with Git, CI/CD, microservices, Docker, and cloud best practices.
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