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AI/ML Engineer with 4+ years of experience building production machine learning systems for connected and autonomous vehicle applications. Experienced in multimodal AI, edge AI, computer vision, real-time inference, predictive diagnostics, graph learning, and generative AI. Skilled in designing scalable ML pipelines, distributed training, GPU-optimized deployment, and cloud-native MLOps using PyTorch, TensorFlow, Kubernetes, AWS, and Azure. Passionate about autonomous systems, simulation-driven AI, robotics, and deploying safety-critical machine learning solutions.…AI/ML Engineer with 4+ years of experience building production machine learning systems for connected and autonomous vehicle applications. Experienced in multimodal AI, edge AI, computer vision, real-time inference, predictive diagnostics, graph learning, and generative AI. Skilled in designing scalable ML pipelines, distributed training, GPU-optimized deployment, and cloud-native MLOps using PyTorch, TensorFlow, Kubernetes, AWS, and Azure. Passionate about autonomous systems, simulation-driven AI, robotics, and deploying safety-critical machine learning solutions.

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AI/ML Engineer with 4+ years of experience building production machine learning systems for connected and autonomous vehicle applications. Experienced in multimodal AI, edge AI, computer vision, real-time inference, predictive diagnostics, graph learning, and generative AI. Skilled in designing scalable ML pipelines, distributed training, GPU-optimized deployment, and cloud-native MLOps using PyTorch, TensorFlow, Kubernetes, AWS, and Azure. Passionate about autonomous systems, simulation-driven AI, robotics, and deploying safety-critical machine learning solutions.…AI/ML Engineer with 4+ years of experience building production machine learning systems for connected and autonomous vehicle applications. Experienced in multimodal AI, edge AI, computer vision, real-time inference, predictive diagnostics, graph learning, and generative AI. Skilled in designing scalable ML pipelines, distributed training, GPU-optimized deployment, and cloud-native MLOps using PyTorch, TensorFlow, Kubernetes, AWS, and Azure. Passionate about autonomous systems, simulation-driven AI, robotics, and deploying safety-critical machine learning solutions.

Available to hire

AI/ML Engineer with 4+ years of experience building production machine learning systems for connected and autonomous vehicle applications. Experienced in multimodal AI, edge AI, computer vision, real-time inference, predictive diagnostics, graph learning, and generative AI. Skilled in designing scalable ML pipelines, distributed training, GPU-optimized deployment, and cloud-native MLOps using PyTorch, TensorFlow, Kubernetes, AWS, and Azure. Passionate about autonomous systems, simulation-driven AI, robotics, and deploying safety-critical machine learning solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

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Expert

Expert

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Intermediate

Intermediate

Work Experience

AI/ML Engineer at HARMAN International

July 1, 2024 - Present

Developed multimodal AI systems for connected vehicle intelligence using vision, audio, CAN telemetry, and transformer-based sensor fusion to enable predictive diagnostics and autonomous decision support. Built production deep learning pipelines for real-time anomaly detection and driver behavior analysis using camera, IMU, GPS, CAN bus, and vehicle telemetry. Designed distributed training pipelines for ViT, temporal fusion transformers, LSTMs, and self-supervised learning on large-scale automotive datasets. Optimized GPU inference using TensorRT and ONNX Runtime, reducing edge inference latency by 35%. Built streaming AI pipelines with AWS IoT Core, Kinesis, and Flink for fleet intelligence. Developed production GraphRAG applications integrating LangGraph, LangChain, vector databases, and knowledge graphs for intelligent diagnostics.

AI Engineer at Uber Technologies Inc.

January 1, 2024 - June 30, 2024

Contributed to an AI-powered graph intelligence platform leveraging GCN, GraphSAGE, and embeddings to identify coordinated marketplace abuse, fake accounts, and payment fraud across riders, drivers, merchants, and devices, improving detection efficiency by 34%. Assisted in designing real-time AWS graph data pipelines for entity resolution and dynamic relationship mapping for trust and safety analytics. Helped fine-tune graph-enhanced LLMs using LoRA and QLoRA on fraud investigation narratives and account abuse patterns, supporting model validation through backtesting and MLflow deployment, achieving 0.91 F1-score and 93% precision.

ML Engineer at KPIT Technologies

October 1, 2019 - December 1, 2021

Developed AI systems for connected and autonomous vehicle platforms using sensor fusion, vehicle telemetry, ADAS, and fleet intelligence. Designed scalable ETL pipelines using Azure Data Factory, PySpark, and SQL for vehicle telemetry and mobility data, processing millions of daily records for analytics and automation. Built and optimized ML models including XGBoost, LightGBM, and autoencoders for predictive maintenance, vehicle health monitoring, energy consumption forecasting, and anomaly detection, improving predictive accuracy by 32% in production. Applied NLP techniques using BERT and TF-IDF logistic regression on diagnostic logs, warranty claims, service reports, and customer feedback to improve issue prioritization by 42%. Containerized and deployed ML solutions using Docker and Azure Kubernetes Service for real-time inference, reducing inference latency by 38%. Developed monitoring dashboards and experimentation frameworks, reducing manual reporting by 49%. Collaborated cross-f