Senior AI/ML Engineer | Data Scientist | GenAI | Production ML Systems
📍 Michigan, USA | Open to Hybrid Roles (Dearborn, Troy, Detroit)

Results-driven AI/ML Engineer and Data Scientist with 8+ years of experience building production-grade AI systems across automotive and enterprise domains.

Currently working at General Motors, designing and deploying scalable AI/ML and GenAI solutions using Python, cloud platforms (GCP/AWS), and modern architectures like RAG, LLMs, and MLOps pipelines to transform large-scale datasets into business-aligned decision systems.

Previously worked across data science and enterprise AI roles, solving real-world problems in domains like automotive, education, and industrial analytics.

Core Focus Areas:

• Predictive Modeling & Advanced Statistical Analysis
• Generative AI (LLMs, RAG, Multimodal Systems)
• Gradient Boosting (XGBoost, LightGBM), Random Forests, GLMs
• Feature Engineering & Model Optimization
• Advanced SQL & Distributed Data Processing
• Experimental Design & A/B Testing
• MLOps & Production ML Deployment

• Python (Pandas, NumPy, Scikit-learn, SciPy)
• Advanced SQL (multi-terabyte datasets)
• PySpark
• Statistical Modeling & Hypothesis Testing

• Supervised & Unsupervised Learning
• Gradient Boosting (XGBoost, LightGBM)
• Random Forest
• Logistic Regression & GLMs
• Clustering & Dimensionality Reduction
• Cross-Validation & Model Evaluation (MAE, RMSE, ROC-AUC)
• Bias-Variance Optimization
• Model Drift Monitoring

• RAG Architectures (Retrieval-Augmented Generation)
• LLMs (Mistral, LLaMA, Multimodal Models)
• Prompt Engineering
• LoRA / QLoRA Fine-tuning
• LangChain, LangGraph
• Vector Databases (Qdrant)

• GCP (BigQuery, Vertex AI)
• AWS (S3, SageMaker, Glue, EC2)
• Azure
• Docker & Kubernetes
• CI/CD (GitHub Actions, Jenkins)
• Terraform
• ML Lifecycle Management & Monitoring

• PostgreSQL, Snowflake, Cassandra
• Hadoop Ecosystem (Spark, Hive)
• Power BI & Tableau

Feb 2023 – Present | Warren, MI

• Architected and deployed GenAI and RAG-based systems for automotive engineering workflows, enabling intelligent data retrieval and reducing manual analysis effort.
• Built multimodal AI pipelines integrating logs, documents, and system data using vector databases (Qdrant) and LLMs.
• Designed scalable ML pipelines on GCP (BigQuery, Vertex AI) for processing large-scale automotive datasets.
• Implemented LLM fine-tuning (LoRA/QLoRA) and optimization techniques (quantization, pruning) to reduce inference cost and improve performance.
• Developed AI-powered applications such as log analyzers, document intelligence systems, and debugging assistants.
• Operationalized models using Docker, Kubernetes, and CI/CD pipelines, ensuring production reliability and scalability.
• Built monitoring and drift detection systems to maintain model performance in production.

May 2017 – Aug 2022 | India

• Developed predictive models improving customer conversion by 30% using ensemble techniques (XGBoost, Random Forest) on large-scale CRM and financial datasets.
• Built forecasting systems to estimate student demand and financial capability for study abroad planning.
• Designed A/B testing frameworks improving campaign performance by 12% through statistically robust experimentation.
• Engineered large-scale analytics pipelines using SQL and Snowflake to improve targeting precision by 15%.
• Built ETL pipelines integrating multi-source data, reducing manual processing effort by 30%.
• Delivered actionable insights to product and marketing teams, enabling data-driven business decisions.

Aug 2018 – Jul 2021

• Developed forecasting and predictive models for demand planning and process optimization.
• Processed large-scale IoT and time-series datasets using Spark and Hadoop.
• Implemented robust model validation using MAE, RMSE, and cross-validation.
• Built distributed data systems handling multi-terabyte workloads.

• Databricks Certified Data Engineer Professional
• Microsoft Certified: Azure Data Engineer Associate
• AWS Certified Data Engineer – Associate

• Scalable Predictive Modeling
• Generative AI & Agentic Systems
• Enterprise RAG Architectures
• Cloud-Native ML Systems
• Automotive AI & SDV Systems

📧 mithundama.de@gmail.com
💼 LinkedIn: https://www.linkedin.com/feed/
🖥️ GitHub: https://github.com/MithunDataPro

Strong models don’t create impact — production-ready, validated, and scalable systems do.