AI Engineer with 3+ years in LLM, RAG & Agentic Systems
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AI Engineer with 4+ years of production backend experience at Ford Motor Company, specialising in LLM-powered and agentic systems. Sole architect of Ford Credit's production AI Customer Support Engine - a six-layer hybrid rule-based and agentic platform, combining a two-stage RAG pipeline, a rule-based complexity gate, and a bounded Claude agent loop that cut LLM costs by 84%. Strong foundation in distributed systems, event-driven architecture (Pub/Sub, CQRS), and GCP-native deployments. Google Cloud Certified.
SRM Easwari Engineering College
B.E. · Engineering
June 1, 2018 – May 1, 2022
Ford Motor Private Limited
Senior Software Developer
August 1, 2022 – March 1, 2026
Chennai, Tamil Nadu, India
Associate Cloud Engineer
Google Cloud Certified
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience at Ford Motor Private Limited, a large enterprise, suggests an ability to operate within structured environments. The breadth of technical skills and the focus on innovative AI solutions indicate a proactive and growth-oriented mindset. The detailed descriptions of problem-solving and cost reduction align with a results-driven culture. While there's no explicit mention of team collaboration or mentorship, the 'sole architect' role implies a capacity for independent contribution. The diversity of projects, from customer communications platforms to advanced AI support engines, shows adaptability and a willingness to tackle varied technical challenges.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their detailed descriptions of architecting complex AI solutions and optimizing costs. Their experience as a 'sole architect' suggests high autonomy and ownership, which aligns well with senior roles requiring independent decision-making. The focus on auditable systems and compliance in a regulated financial services environment indicates a meticulous and responsible approach to engineering. The lack of specific psychometric or English test scores makes it difficult to fully assess work attitude, stress handling, or team collaboration, but the project descriptions imply a high level of technical communication within project contexts.