AI/ML ENGINEER

GANESH DHANAVATH

AI/ML Engineer

+1-551-***-**** *******************@*****.*** LinkedIn Github

PROFESSIONAL SUMMARY

AI/ML Engineer with 6+ years of experience building and productionizing machine learning and LLM systems in cloud environments. Expertise in designing end-to-end ML pipelines including feature engineering, training, retraining, batch and real-time inference, and large-scale model monitoring. Strong experience deploying ML solutions using Python, SQL, Databricks, AWS, and Azure, leveraging frameworks such as PyTorch, TensorFlow, XGBoost, and HuggingFace. Experienced in operationalizing LLM and RAG systems using LangChain and LlamaIndex to support AI-driven decision platforms. Proven track record collaborating with data scientists, data engineers, and product teams to productionize models and deliver reliable AI systems across healthcare and enterprise environments.

EXPERIENCE

AI/ML Engineer Stryker Dallas,Texas Sep 2024 - Present

Developed end-to-end ML pipelines on Azure Databricks to support healthcare supply chain planning and operational decision systems, delivering multi-million annualized cost reductions.

Built scalable feature engineering pipelines and feature management workflows supporting model training, inference, and historical replay across healthcare operational datasets.

Designed batch and real-time inference systems using REST APIs and containerized services to deliver low-latency predictions across enterprise planning and hospital workflow systems.

Implemented automated training and retraining workflows using MLflow and orchestrated pipelines to ensure continuous model improvement and production reliability.

Built model observability pipelines to monitor prediction drift, latency, feature quality, and system reliability across production deployments.

Deployed open-source LLMs (LLaMA family / Llama 3.x) in production using Azure ML and AKS, enabling scalable, GPU-backed inference with optimized latency and throughput.

Integrated ML pipelines with customer data platforms (CDP) to enable unified customer views and downstream analytics.

Built ML models and AI copilots supporting hospital operations and healthcare supply chain decision systems.

Implemented containerized ML deployments on Kubernetes (AKS) with CI/CD pipelines, enabling scalable production model serving and reducing deployment time by 40%.

Piloted a GenAI Supply Chain Planner Copilot using RAG architecture, enabling planners to query operational data and receive grounded responses backed by enterprise data sources.

Built production-grade RAG pipelines over healthcare documents using LangChain, vector databases, and LLM frameworks to improve information retrieval accuracy.

Designed document extraction pipelines combining OCR and LLMs to process healthcare PDFs and structured documents, improving ingestion accuracy and downstream model inputs.

Implemented advanced prompting techniques including Chain-of-Thought reasoning, multi-step prompt chaining, and structured outputs to improve LLM response accuracy.

ML Engineer Quantium Hyderabad, India June 2021 - July 2023

Developed and deployed scalable machine learning models for retail, banking, and telecommunications clients, improving business outcomes by up to 20%.

Collaborated with cross-functional teams including data scientists, analysts, and product managers to deliver end-to- end AI solutions using Python, SQL, Spark, and AWS.

Engineered robust data pipelines to process and model terabytes of structured and unstructured data.

Conducted advanced A/B testing and causal inference to validate model impact and drive client ROI.

Designed feature pipelines and reusable feature engineering components supporting scalable ML workflows across multiple production models.

Implemented model monitoring, retraining pipelines, and performance tracking dashboards using tools like MLflow, Airflow, and Tableau/Power BI.

Worked on optimizing data preprocessing pipelines, ensuring the efficient handling of large datasets and improving the overall performance of machine learning models.

Worked with proprietary and third-party datasets, ensuring privacy compliance and robust feature engineering practices.

Contributed to the improvement of internal ML frameworks and tools, enhancing reusability and reducing deployment time by 30%.

Implemented RAG and Agent evaluation frameworks to measure retrieval precision, answer similarity, hallucination rate, and end-to-end task success for LLM-based applications, enabling continuous model and prompt optimization.

Data Scientist Tiger Analytics Hyderabad, India May 2018 May 2021

Built and fine-tuned supervised and unsupervised models using scikit-learn, TensorFlow, PyTorch, and XGBoost for applications like fraud detection, customer segmentation, and price optimization.

Operationalized models into production using Docker, Kubernetes, and Azure cloud platform, reducing deployment latency by 35%.

Automated model retraining and monitoring processes with MLflow and Prometheus, ensuring stability and drift detection.

Conducted exploratory data analysis and hypothesis testing to extract actionable insights from client data, using Pandas, Seaborn, and Plotly.

Built custom dashboards in Power BI and Tableau to present KPIs and model outputs to executive-level stakeholders.

Prototyped reusable components for feature engineering, hyperparameter tuning, and model explainability, reducing turnaround time for new projects by 20%.

Created knowledge-sharing presentations and internal white papers on best practices in MLOps and model governance.

Designed and implemented end-to-end data pipelines using Apache Airflow, SQL, and PySpark, improving data refresh cycles and enabling real-time analytics.

Developed and evaluated Text-to-SQL pipelines using LLMs, validating query accuracy, execution correctness, and result consistency against ground-truth datasets.

SKILLS

AI Technologies and Frameworks: Langchain, Langsmith, LangGraph, Llama Index, Hugging Face, AutoGen, Crew AI, Prompt Engineering, RAG, Agentic AI, Agent Evaluation, RAG Evaluation, Text2SQL Evaluation, OCR, Model Observability, Real-time Inference, Batch Inference,Feature Store

Data Science & Machine Learning: Predictive Analytics, NLP, Deep Learning, Model Deployment

Programming Languages: Python (Flask, FastAPI), R, SQL, Java

Cloud Platforms: AWS (EC2, S3, Sage Maker, Bed Rock, Lambda, Cloud watch, IAM), GCP (Vertex AI, Big Query, IAM), Snowflake, Databricks, Azure OpenAI

Databases: SQL Server, MongoDB, Pinecone, Milvus, Neo4j, SAP B/4 HANA

Data Engineering: ETL, Data Warehousing, Big Data (Hadoop, Spark)

Tools & Technologies: Tensor Flow, Pytorch, Scikit-Learn, Pandas, Numpy, Tableau, Power BI, Kubeflow

Project Management: Waterfall, Agile, Scrum, JIRA

Soft Skills: Leadership, Team Management, Strategic Planning, Communication

CERTIFICATIONS & ACHIEVEMENTS

Microsoft Azure AI Engineer Associate

AWS Certified Machine Learning Engineer - Associate Databricks Certified Machine Learning Associate

EDUCATION

Master s From Sacred Heart University, CT

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