Senior Technical Lead - Data & AI

Tätigkeitsbereich:Produktion Fachabteilung:Cloud, Data \& Connected Services Gesellschaft:Mercedes\-Benz Research and Development India Private Limited Standort:Mercedes\-Benz Research and Development India Private Limited, Bangalore Startdatum:sofort Veröffentlichungsdatum:04\.06\.2026 Stellennummer:MER000434V Arbeitszeit:Vollzeit Aufgaben Mercedes\-Benz Connected Cars is building next \-generation Data \& AI Platforms to power intelligent mobility, vehicle insights, and AI \-driven digital experiences. We are looking for a Senior Data \& AI Engineer to design, build, and scale enterprise \-grade AI/ML and Generative AI solutions. This role plays a critical part in enabling platform \-driven AI adoption, ensuring solutions are production\-ready, governed, obser vable, and cloud\-native. Key Responsibilities AI/ML \& Generative AI Engineering Design and implement scalable Machine Learning and Generative AI solutions on Databricks and cloud platforms Build and optimize feature pipelines, training workflows, and inference systems Develop reusable AI services, APIs, and enterprise AI accelerators Contribute to advanced GenAI use cases including copilots, assistants, and autonomous workflows Data \& AI Platform Engineering (MLOps) Build and manage end \-to\-end ML pipelines (training validation deployment monit oring) Implement CI/CD for ML models and AI \-driven applications Integrate with Databricks ecosystem (MLflow, Unity Catalog, Workflows, Model Serving) Enable standardized platform capabilities for scalable AI adoption Data Engineering \& Integration Design and optimize batch and real \-time data pipelines using Spark and streaming frameworks Work with structured and unstructured datasets across large \-scale systems Ensure data quality, governance, and performance optimization Implement medallion architecture (Bronze / Silver / Gold) and incremental data processing strategies AI Governance, Observability \& Security Implement model monitoring, drift detection, and performance tracking Ensure compliance with enterprise AI governance, security, and regulatory standards Enable lineage, auditability, and observability across AI pipelines and services Core Technical Expertise Databricks \& Data Platform Engineering Strong experience with Databricks ecosystem (Delta Lake, DLT, Unity Catalog, Mosaic AI) Expertise in building scalable pipelines using Apache Spark, Structured Streaming, Kafka Advanced knowledge of data modeling, schema evolution, and CDC Experience in performance tuning, workload optimization, and cost efficiency Generative AI \& Foundation Models Hands\-on experience with enterprise use of LLMs (OpenAI, Anthropic, Llama, Gemini, Mistral) Experience with fine \-tuning, LoRA/QLoRA, quantization, and inference optimization Strong understanding of LLMOps, prompt engineering lifecycle, and evaluation pipelines Implementation of guardrails, hallucination mitigation, and output validation RAG \& Knowledge Systems Design and build production \-grade Retrieval \-Augmented Generation (RAG) systems Experience with vector databases (FAISS, Pinecone, Milvus, Databricks Vector Search) Knowledge of embedding models, semantic search, and knowledge indexing pipelines Expertise in context optimization, chunking, and hybrid retrieval strategies AI Agents \& Autonomous Systems Design agentic AI systems with workflow orchestration and reasoning capabilities Experience with multi \-agent systems, task decomposition, and collaboration patterns Hands\-on experience with LangChain, LangGraph, AutoGen or similar frameworks Knowledge of memory management and agent coordination strategies AI Integration \& Platform Architecture Experience with REST/gRPC APIs and event \-driven AI services Understanding of enterprise AI integration and service \-oriented architecture Exposure to Model Context Protocol (MCP) and AI communication standards Experience in distributed AI workflows and agent orchestration Cloud \& Platform Engineering Experience in real \-time and batch inference pipelines Hands\-on with Docker, Kubernetes, Helm \-based deployments Knowledge of autoscaling, serving infrastructure, and cost optimization Familiarity with Databricks AI stack (Mosaic AI, AI Gateway, Lakehouse Monitoring) Required Qualifications 8\+ years of experience in Data Engineering / ML Engineering / AI Platforms Strong expertise in Databricks, Spark, and cloud data platforms Experience building production \-grade AI/ML systems and pipelines Solid understanding of distributed systems and platform architecture Strong problem\-solving and system design capabilities Excellent collaboration and stakeholder engagement skills Preferred Qualifications Experience in Connected Cars / IoT / Automotive data platforms Exposure to AI copilots and intelligent assistants Knowledge of semantic data discovery and metadata \-driven AI systems Experience in LLM\-powered analytics or observability platforms Certifications in Databricks, AWS, or Azure What We Offer Opportunity to shape AI \-driven Connected Car experiences at scale Work on cutting \-edge GenAI, agentic systems, and data platforms Collaborative, innovation \-led engineering environment Global exposure within Mercedes \-Benz digital ecosyste Qualifikationen Mercedes\-Benz Connected Cars is building next \-generation Data \& AI Platforms to power intelligent mobility, vehicle insights, and AI \-driven digital experiences. We are looking for a Senior Data \& AI Engineer to design, build, and scale enterprise \-grade AI/ML and Generative AI solutions. This role plays a critical part in enabling platform \-driven AI adoption, ensuring solutions are production\-ready, governed, obser vable, and cloud\-native. Key Responsibilities AI/ML \& Generative AI Engineering Design and implement scalable Machine Learning and Generative AI solutions on Databricks and cloud platforms Build and optimize feature pipelines, training workflows, and inference systems Develop reusable AI services, APIs, and enterprise AI accelerators Contribute to advanced GenAI use cases including copilots, assistants, and autonomous workflows Data \& AI Platform Engineering (MLOps) Build and manage end \-to\-end ML pipelines (training validation deployment monit oring) Implement CI/CD for ML models and AI \-driven applications Integrate with Databricks ecosystem (MLflow, Unity Catalog, Workflows, Model Serving) Enable standardized platform capabilities for scalable AI adoption Data Engineering \& Integration Design and optimize batch and real \-time data pipelines using Spark and streaming frameworks Work with structured and unstructured datasets across large \-scale systems Ensure data quality, governance, and performance optimization Implement medallion architecture (Bronze / Silver / Gold) and incremental data processing strategies AI Governance, Observability \& Security Implement model monitoring, drift detection, and performance tracking Ensure compliance with enterprise AI governance, security, and regulatory standards Enable lineage, auditability, and observability across AI pipelines and services Core Technical Expertise Databricks \& Data Platform Engineering Strong experience with Databricks ecosystem (Delta Lake, DLT, Unity Catalog, Mosaic AI) Expertise in building scalable pipelines using Apache Spark, Structured Streaming, Kafka Advanced knowledge of data modeling, schema evolution, and CDC Experience in performance tuning, workload optimization, and cost efficiency Generative AI \& Foundation Models Hands\-on experience with enterprise use of LLMs (OpenAI, Anthropic, Llama, Gemini, Mistral) Experience with fine \-tuning, LoRA/QLoRA, quantization, and inference optimization Strong understanding of LLMOps, prompt engineering lifecycle, and evaluation pipelines Implementation of guardrails, hallucination mitigation, and output validation RAG \& Knowledge Systems Design and…