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AI/ML Engineer with 5+ years of experience delivering Generative AI (including RAG and agentic systems), machine learning, and enterprise data platform solutions across financial and healthcare domains. I build production-grade pipelines—from retrieval, embeddings, prompt/orchestration, safety guardrails, and evaluation to scalable model serving and observability. I’m skilled in Python-based ML/NLP workflows (PyTorch, Scikit-learn, Spark), MLOps (MLflow, Docker, Kubernetes, CI/CD), and secure API development (FastAPI/microservices) across AWS/Azure/GCP. I focus on improving latency, accuracy, governance, and reliability through monitoring, drift detection, and automated retraining.…AI/ML Engineer with 5+ years of experience delivering Generative AI (including RAG and agentic systems), machine learning, and enterprise data platform solutions across financial and healthcare domains. I build production-grade pipelines—from retrieval, embeddings, prompt/orchestration, safety guardrails, and evaluation to scalable model serving and observability. I’m skilled in Python-based ML/NLP workflows (PyTorch, Scikit-learn, Spark), MLOps (MLflow, Docker, Kubernetes, CI/CD), and secure API development (FastAPI/microservices) across AWS/Azure/GCP. I focus on improving latency, accuracy, governance, and reliability through monitoring, drift detection, and automated retraining.

Rama Laxmi

AI Engineer, Data Scientist, Data Engineer

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AI/ML Engineer with 5+ years of experience delivering Generative AI (including RAG and agentic systems), machine learning, and enterprise data platform solutions across financial and healthcare domains. I build production-grade pipelines—from retrieval, embeddings, prompt/orchestration, safety guardrails, and evaluation to scalable model serving and observability. I’m skilled in Python-based ML/NLP workflows (PyTorch, Scikit-learn, Spark), MLOps (MLflow, Docker, Kubernetes, CI/CD), and secure API development (FastAPI/microservices) across AWS/Azure/GCP. I focus on improving latency, accuracy, governance, and reliability through monitoring, drift detection, and automated retraining.…AI/ML Engineer with 5+ years of experience delivering Generative AI (including RAG and agentic systems), machine learning, and enterprise data platform solutions across financial and healthcare domains. I build production-grade pipelines—from retrieval, embeddings, prompt/orchestration, safety guardrails, and evaluation to scalable model serving and observability. I’m skilled in Python-based ML/NLP workflows (PyTorch, Scikit-learn, Spark), MLOps (MLflow, Docker, Kubernetes, CI/CD), and secure API development (FastAPI/microservices) across AWS/Azure/GCP. I focus on improving latency, accuracy, governance, and reliability through monitoring, drift detection, and automated retraining.

Available to hire

AI/ML Engineer with 5+ years of experience delivering Generative AI (including RAG and agentic systems), machine learning, and enterprise data platform solutions across financial and healthcare domains. I build production-grade pipelines—from retrieval, embeddings, prompt/orchestration, safety guardrails, and evaluation to scalable model serving and observability.

I’m skilled in Python-based ML/NLP workflows (PyTorch, Scikit-learn, Spark), MLOps (MLflow, Docker, Kubernetes, CI/CD), and secure API development (FastAPI/microservices) across AWS/Azure/GCP. I focus on improving latency, accuracy, governance, and reliability through monitoring, drift detection, and automated retraining.

Skills

Experience Level

Expert

Expert

Expert

Expert

Work Experience

AI Engineer at Wizards of the Coast

May 1, 2025 - Present

Built end-to-end ML and Generative AI solutions including document processing, embedding pipelines, and production RAG architectures (chunking, retrievers, prompt templates, grounding). Improved RAG indexing time by 60% via optimized chunking and async embedding workflows. Developed low-latency inference APIs using FastAPI, deployed on AWS SageMaker and GCP Vertex AI with monitoring and retraining. Implemented agentic components with LangChain/LangGraph and integrated tool access via MCP. Established MLOps workflows using MLflow, CI/CD, Docker/Kubernetes, canary/blue-green deployments, drift detection, and automated retraining. Conducted red-team/adversarial prompt evaluations to mitigate hallucinations and harmful outputs. Optimized distributed training/inference with Ray, PyTorch, and Kubernetes to improve GPU utilization by 38%. Built self-healing Kubernetes operators and observability pipelines (OpenTelemetry/Grafana/Kafka) for traceability across large microservice fleets. Ensured

AI/ML Engineer at CME Group

August 1, 2024 - April 1, 2025

Designed and optimized Snowflake SQL/data models to support AI/ML and RAG workloads for enterprise financial use cases. Built and integrated PyTorch inference services with backend APIs for real-time market intelligence and batch analytics. Implemented grounded RAG combining embeddings, semantic retrieval, document ingestion, context injection, LLM inference, and post-processing using internal trading policies and regulatory/knowledge repositories. Fine-tuned PyTorch models for NLP classification, sentiment analysis, risk assessment, and predictive analytics. Built supervised ML models for fraud detection, market trend prediction, and anomaly detection using XGBoost/Random Forest/Scikit-learn. Developed secure AWS architectures with VPC, encryption, and IAM to meet regulatory compliance needs. Reduced analyst research time by 35% using multi-agent summarization and intelligent retrieval workflows. Exposed GenAI capabilities via FastAPI/Docker REST microservices and implemented explaina

Data Scientist at MedVantage Analytics

May 1, 2020 - July 31, 2023

Delivered healthcare/insurance ML solutions translating business needs into predictive analytics and operational decision-making. Built and deployed models for classification, regression, risk scoring, fraud detection, and forecasting using Scikit-learn and PyTorch. Performed EDA, feature engineering/selection, statistical analysis, and validation to improve outcomes. Created scalable ETL and data processing pipelines using Python, SQL, Pandas, PySpark, and Apache Spark for structured and unstructured data. Built reusable training pipelines for preprocessing, feature generation, hyperparameter tuning, evaluation, and batch inference. Developed REST APIs using FastAPI/Flask and deployed containerized services with Docker on Kubernetes. Implemented MLOps with MLflow, Git, CI/CD for versioning, reproducibility, monitoring, logging, drift detection, and alerts. Optimized SQL/data warehouse queries for reporting performance and downstream AI/ML workloads; documented workflows for governance