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

Principal Machine Learning & AI Engineer with 9+ years of hands-on experience architecting and deploying production-grade AI systems across Healthcare, Financial Services, E-commerce, and Autonomous Systems. Deep expertise in LLMs/GenAI, RAG architectures, deep learning, and end-to-end MLOps pipelines with a strong focus on compliant model governance. Proven track record leading cross-functional teams across product, data, and engineering to deliver measurable business outcomes. Experienced with RLHF, fine-tuning (LoRA/QLoRA), vector search, real-time monitoring, model risk management (HIPAA/GDPR/SOC 2), and building scalable, low-latency AI services in Kubernetes-based production environments.…Principal Machine Learning & AI Engineer with 9+ years of hands-on experience architecting and deploying production-grade AI systems across Healthcare, Financial Services, E-commerce, and Autonomous Systems. Deep expertise in LLMs/GenAI, RAG architectures, deep learning, and end-to-end MLOps pipelines with a strong focus on compliant model governance. Proven track record leading cross-functional teams across product, data, and engineering to deliver measurable business outcomes. Experienced with RLHF, fine-tuning (LoRA/QLoRA), vector search, real-time monitoring, model risk management (HIPAA/GDPR/SOC 2), and building scalable, low-latency AI services in Kubernetes-based production environments.

annmoh43

AI Engineer, Data Scientist, Data Engineer, +2





Principal Machine Learning & AI Engineer with 9+ years of hands-on experience architecting and deploying production-grade AI systems across Healthcare, Financial Services, E-commerce, and Autonomous Systems. Deep expertise in LLMs/GenAI, RAG architectures, deep learning, and end-to-end MLOps pipelines with a strong focus on compliant model governance. Proven track record leading cross-functional teams across product, data, and engineering to deliver measurable business outcomes. Experienced with RLHF, fine-tuning (LoRA/QLoRA), vector search, real-time monitoring, model risk management (HIPAA/GDPR/SOC 2), and building scalable, low-latency AI services in Kubernetes-based production environments.…Principal Machine Learning & AI Engineer with 9+ years of hands-on experience architecting and deploying production-grade AI systems across Healthcare, Financial Services, E-commerce, and Autonomous Systems. Deep expertise in LLMs/GenAI, RAG architectures, deep learning, and end-to-end MLOps pipelines with a strong focus on compliant model governance. Proven track record leading cross-functional teams across product, data, and engineering to deliver measurable business outcomes. Experienced with RLHF, fine-tuning (LoRA/QLoRA), vector search, real-time monitoring, model risk management (HIPAA/GDPR/SOC 2), and building scalable, low-latency AI services in Kubernetes-based production environments.

Available to hire

Principal Machine Learning & AI Engineer with 9+ years of hands-on experience architecting and deploying production-grade AI systems across Healthcare, Financial Services, E-commerce, and Autonomous Systems. Deep expertise in LLMs/GenAI, RAG architectures, deep learning, and end-to-end MLOps pipelines with a strong focus on compliant model governance.

Proven track record leading cross-functional teams across product, data, and engineering to deliver measurable business outcomes. Experienced with RLHF, fine-tuning (LoRA/QLoRA), vector search, real-time monitoring, model risk management (HIPAA/GDPR/SOC 2), and building scalable, low-latency AI services in Kubernetes-based production environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Principal Machine Learning Engineer at Lateetud

August 1, 2025 - Present

Engineered an enterprise-grade Retrieval-Augmented Generation (RAG) pipeline for a Fortune-500 client's internal knowledge base using LangChain, Pinecone, and GPT-4; delivered sub-200ms p99 latency via FastAPI microservices on Kubernetes. Led domain-adaptive fine-tuning of LLaMA 2 (13B) and Mistral 7B using QLoRA on 4x A100 GPUs with custom RLHF reward modeling via Label Studio; reduced hallucinations by 34% using automated evaluation (including BERTScore and human benchmarks). Built multimodal document understanding combining CLIP for vision-language embeddings and OpenAI Whisper for audio transcription; deployed across 15 enterprise clients with 500K+ documents/month and 94% extraction accuracy. Designed and operated MLOps on Kubernetes + Kubeflow with MLflow tracking, model versioning, and automated retraining; reduced deployment cycle from 3 weeks to 4 days. Implemented real-time monitoring using Evidently AI and Prometheus/Grafana; reduced data drift incidents by 58% and maintaine

Senior Machine Learning Engineer at Analytics8

May 1, 2022 - August 1, 2025

Built a real-time fraud detection system for a Tier-1 bank processing 800K+ transactions/day using an ensemble of XGBoost and LightGBM on streaming data via Kafka + Flink; achieved <5ms scoring latency and 97.3% precision at 0.1% false-positive rate, preventing $47M in annual losses. Engineered NLP pipeline using spaCy and fine-tuned BERT on SEC filings to extract 40+ structured financial indicators from 10-K/10-Q documents; reduced analyst extraction time by 80%. Deployed Temporal Fusion Transformer and Prophet-based forecasting models for credit risk scoring across a $2.1B loan portfolio; improved 12-month default prediction AUC from 0.74 to 0.88. Implemented a centralized Feature Store on Feast backed by Redis (online) and BigQuery (offline), standardizing 200+ features across 40+ models and reducing feature latency by 70%. Standardized explainability with SHAP/LIME for regulatory-grade documentation aligned to U.S. SR 11-7 and OCC model risk guidance. Collaborated on RL agents for

Machine Learning Engineer at Creole Studios

April 1, 2020 - May 1, 2022

Developed a 3D medical image segmentation pipeline for radiology CT scans using U-Net with ResNet-50. Built a clinical NLP classifier leveraging BioBERT fine-tuned on 500K de-identified MIMIC-III notes to predict 30-day readmission risk; reduced readmission rates by 22% across 6 hospital networks and generated $4.1M in avoided CMS penalty costs. Implemented privacy-preserving federated learning using PySyft and TensorFlow Federated to train across 12 hospital networks without centralizing PHI; achieved 89% centralized-training accuracy while remaining HIPAA compliant. Deployed HIPAA-compliant real-time inference for 18 clinical decision-support tools on AWS EC2 using TensorFlow Serving behind NGINX with TLS, VPC isolation, and SOC 2 Type II compliant audit logging.

Machine Learning Engineer at BigChalk

May 1, 2018 - April 1, 2020

Architected a two-tower recommendation engine using Spark MLlib ALS and neural matrix factorization for 5M monthly active users; increased AOV by 18% and CTR by 31% through personalized ranking. Improved lung nodule segmentation with TensorFlow on 8x V100 GPUs, achieving 91.4% Dice coefficient surpassing radiologist baseline of 87%. Built a demand forecasting system combining ARIMA baselines with LSTM and Temporal Convolutional Networks for 50K+ SKUs across 8 distribution centers; reduced overstock costs by $3.2M annually and improved fill rate from 91% to 97%. Designed automated A/B testing and experimentation infrastructure using Python, Statsmodels, and Thompson Sampling to support 30+ concurrent experiments with sequential testing to reduce time-to-significance by 40%.