Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I’m an AI/ML and Generative AI Engineer with 6+ years of experience building production-grade machine learning, deep learning, NLP, computer vision, and LLM-based systems. I’ve worked across autonomous driving, healthcare, banking, and insurance—designing scalable pipelines, improving inference performance, and delivering reliable model deployments through strong MLOps practices. I focus on end-to-end delivery: fine-tuning and evaluating LLMs using LoRA/QLoRA, implementing RAG and vector search solutions, and building agentic workflows for automation and decision support. I also prioritize production readiness—monitoring model drift and data quality, troubleshooting latency and failures, and implementing guardrails, CI/CD, and monitoring to keep systems stable and measurable in real-world environments.…I’m an AI/ML and Generative AI Engineer with 6+ years of experience building production-grade machine learning, deep learning, NLP, computer vision, and LLM-based systems. I’ve worked across autonomous driving, healthcare, banking, and insurance—designing scalable pipelines, improving inference performance, and delivering reliable model deployments through strong MLOps practices. I focus on end-to-end delivery: fine-tuning and evaluating LLMs using LoRA/QLoRA, implementing RAG and vector search solutions, and building agentic workflows for automation and decision support. I also prioritize production readiness—monitoring model drift and data quality, troubleshooting latency and failures, and implementing guardrails, CI/CD, and monitoring to keep systems stable and measurable in real-world environments.

Nithin Kumar Pasam

AI Engineer, Data Scientist, Software Engineer, +4





I’m an AI/ML and Generative AI Engineer with 6+ years of experience building production-grade machine learning, deep learning, NLP, computer vision, and LLM-based systems. I’ve worked across autonomous driving, healthcare, banking, and insurance—designing scalable pipelines, improving inference performance, and delivering reliable model deployments through strong MLOps practices. I focus on end-to-end delivery: fine-tuning and evaluating LLMs using LoRA/QLoRA, implementing RAG and vector search solutions, and building agentic workflows for automation and decision support. I also prioritize production readiness—monitoring model drift and data quality, troubleshooting latency and failures, and implementing guardrails, CI/CD, and monitoring to keep systems stable and measurable in real-world environments.…I’m an AI/ML and Generative AI Engineer with 6+ years of experience building production-grade machine learning, deep learning, NLP, computer vision, and LLM-based systems. I’ve worked across autonomous driving, healthcare, banking, and insurance—designing scalable pipelines, improving inference performance, and delivering reliable model deployments through strong MLOps practices. I focus on end-to-end delivery: fine-tuning and evaluating LLMs using LoRA/QLoRA, implementing RAG and vector search solutions, and building agentic workflows for automation and decision support. I also prioritize production readiness—monitoring model drift and data quality, troubleshooting latency and failures, and implementing guardrails, CI/CD, and monitoring to keep systems stable and measurable in real-world environments.

Available to hire

I’m an AI/ML and Generative AI Engineer with 6+ years of experience building production-grade machine learning, deep learning, NLP, computer vision, and LLM-based systems. I’ve worked across autonomous driving, healthcare, banking, and insurance—designing scalable pipelines, improving inference performance, and delivering reliable model deployments through strong MLOps practices.

I focus on end-to-end delivery: fine-tuning and evaluating LLMs using LoRA/QLoRA, implementing RAG and vector search solutions, and building agentic workflows for automation and decision support. I also prioritize production readiness—monitoring model drift and data quality, troubleshooting latency and failures, and implementing guardrails, CI/CD, and monitoring to keep systems stable and measurable in real-world environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Advanced

Hindi

Intermediate

Work Experience

AI/ML & Generative AI Engineer at Waymo

December 1, 2024 - Present

Designed and implemented scalable ML/Deep Learning pipelines in Python, PyTorch/TensorFlow, Apache Spark, and AWS to process large autonomous driving datasets (LiDAR, camera, radar, simulation), cutting feature extraction latency by 30%. Developed and optimized Transformer-based multimodal perception/prediction workflows using pruning, quantization, and ONNX Runtime (INT8/INT4) to improve production inference efficiency. Built agentic AI workflows with LangChain/LangGraph/LangSmith and AWS Bedrock to automate scenario mining, labeling, simulation triage, and engineering investigations—improving automation coverage by 40% and reducing manual review effort by 55%. Implemented Neo4j knowledge graphs for scene/entity relationships to improve edge-case discovery and scenario retrieval. Delivered MLOps using SageMaker, Kubernetes, PyTorch Serve, Docker, and GitHub Actions/CI-CD, reducing deployment time by 25% and enabling monitoring and rollback. Troubleshot model drift, inference latency

AI/ML Engineer at Humana (TCS)

December 1, 2021 - July 31, 2023

Architected and optimized 10+ end-to-end ML pipelines using Python/R/Scikit-learn and Azure services, improving pipeline efficiency by 30%, reducing deployment time by 20%, and increasing model stability by 15%. Built production REST APIs with FastAPI/Flask to expose ML models and analytics services, reducing inference latency by 25% and improving integration reliability across microservices. Implemented cloud-based ML and Generative AI solutions using Azure Machine Learning, AKS, Azure OpenAI, Docker, and Kubernetes while maintaining HIPAA/PHI compliance. Developed NLP and GenAI capabilities using transformer models, embeddings, semantic search, prompt engineering, and RAG for intelligent document analysis and faster retrieval. Troubleshot production issues including pipeline failures, model degradation, API timeouts, feature mismatches, latency, container failures, and data-quality anomalies using structured logging and root-cause analysis. Implemented MLOps/CI-CD with experiment tra

Machine Learning Engineer at Mphasis

July 1, 2019 - November 30, 2021

Built scalable data preprocessing, feature engineering, and ML pipelines using Python, Pandas/NumPy, and Scikit-learn for structured and unstructured enterprise data. Developed predictive analytics and anomaly detection models for banking and insurance, reducing manual data analysis by 30% and improving decision-making. Implemented CI/CD using Jenkins, Git, Docker, and Kubernetes to automate build, testing, containerization, and deployment—improving release consistency and reducing deployment failures. Developed a cloud-native ML platform on GCP Vertex AI, BigQuery, and Cloud Run, reducing training costs by 30% and accelerating deployment by 40%. Added production monitoring and troubleshooting with the ELK stack, identifying API failures, data bottlenecks, and model/container issues; supported incident response and root-cause analysis, reducing MTTR by 20% through improved runbooks, alerting, and diagnostics. Worked with banking/insurance stakeholders and cross-functional teams to ga