Hybrid Principal Engineer

at Ford Motor Company

• Job title: Principal Engineer
• Job type: Full Time
• Experience level: Lead
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: BigQuery, Cloud, Distributed Systems, Google Cloud Platform, IoT, Kafka, SDLC
• Location requirements: Hybrid, Chennai, India

About the role

• Principal Engineer focusing on AI-driven data architecture in Ford's Connected Vehicle Data Platform. Leading design of resilient streaming architectures for proactive business decisions using AI/ML technology.

Responsibilities

• AI-Driven Data Architecture: Lead the design and evolution of a data architecture that natively integrates AI/ML. Architect systems where data is "AI-ready" from ingestion, ensuring seamless compatibility with Large Language Models (LLMs) and predictive analytics.
• Agentic Flows & AI Agents: Design and develop autonomous Agentic flows and AI agents tasked with intelligent data collection, automated metadata tagging, and real-time anomaly detection. Build frameworks where agents can generate proactive insights from vehicle telemetry to identify issues before they lead to recalls or warranty claims.
• Technical Leadership & Mentorship: Act as the primary technical lead for a cross-functional group of software and data engineers. Provide deep technical mentorship in both traditional data engineering and modern applied AI/ML practices.
• Architectural Strategy: Define the long-term roadmap for the platform, ensuring it can handle high-throughput, low-latency streaming data from millions of vehicles while supporting complex AI-driven workloads.
• Data Product Innovation: Lead the transition from raw data pipelines to "Intelligent Data Products," ensuring vehicle data is high-quality, democratized, and enriched by AI for global compliance and business use cases.
• Hands-on Engineering: Lead by example by contributing to critical codebase components, prototyping Agentic frameworks, and solving the most complex technical bottlenecks in our AI and data pipelines.
• Platform Scalability & Reliability: Oversee the design of highly available, fault-tolerant pipelines that scale elastically to eliminate data fragmentation across the Ford enterprise.
• Engineering Standards: Establish best practices for MLOps and LLMOps, ensuring that AI models and agents are deployed, monitored, and versioned with the same rigor as production code.
• Compliance & Global Standards: Partner with security and compliance teams to ensure AI-driven data use cases meet evolving global regulations and protect customer privacy.

Requirements

• Education: Minimum – Bachelor’s Degree in Computer Science, Information Technology, or a related field. Preferred – Master’s Degree in a highly technical field (Computer Science, Mathematics, or Physics) with a focus on AI/ML.
• Experience: 15+ years of experience in software and data engineering, with at least 5 years focused on large-scale connected vehicle or IoT streaming platforms.
• AI/ML Expertise: Proven experience in Applied AI, including the development of Agentic workflows (e.g., using frameworks like LangChain, CrewAI, or AutoGPT) and the integration of LLMs into enterprise data pipelines.
• Data Architecture: Deep understanding of modern data architectural patterns (Data Lakehouse, Mesh) and how to optimize them for AI/ML consumption.
• Technical Authority: Previous experience as a Principal or Staff-level engineer leading the architecture of enterprise-scale distributed systems.
• Cloud & AI Stack: Expert-level experience with Google Cloud Platform (GCP), specifically BigQuery, Dataflow, and Vertex AI. Experience with Vector Databases (e.g., Pinecone, Weaviate) and RAG (Retrieval-Augmented Generation) architectures.
• Streaming & Messaging: Deep expertise in streaming technologies (Kafka, Pub/Sub) and managing high-throughput topics for real-time AI inference.
• DevOps & MLOps: Expert knowledge of SDLC, CI/CD, and MLOps frameworks to manage the lifecycle of AI agents and data models.
• Optimization: Ability to optimize compute costs for both high-scale data processing and AI/ML inference in cloud environments.

Benefits

• Competitive compensation, benefits, and the opportunity to lead the next generation of automotive intelligence.