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.