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I am Esha Gangam, an AI/ML Engineer based in New York, specializing in designing, deploying, and operationalizing scalable machine learning and Generative AI solutions across enterprise environments. With nearly 4 years of experience, I have delivered end-to-end pipelines, built LLM-powered applications, and established MLOps practices that improve efficiency, reduce manual work, and enable data-driven decision-making.…I am Esha Gangam, an AI/ML Engineer based in New York, specializing in designing, deploying, and operationalizing scalable machine learning and Generative AI solutions across enterprise environments. With nearly 4 years of experience, I have delivered end-to-end pipelines, built LLM-powered applications, and established MLOps practices that improve efficiency, reduce manual work, and enable data-driven decision-making.

Esha Gangam

Developer, Programmer, Web Developer, +5

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I am Esha Gangam, an AI/ML Engineer based in New York, specializing in designing, deploying, and operationalizing scalable machine learning and Generative AI solutions across enterprise environments. With nearly 4 years of experience, I have delivered end-to-end pipelines, built LLM-powered applications, and established MLOps practices that improve efficiency, reduce manual work, and enable data-driven decision-making.…I am Esha Gangam, an AI/ML Engineer based in New York, specializing in designing, deploying, and operationalizing scalable machine learning and Generative AI solutions across enterprise environments. With nearly 4 years of experience, I have delivered end-to-end pipelines, built LLM-powered applications, and established MLOps practices that improve efficiency, reduce manual work, and enable data-driven decision-making.

Available to hire

I am Esha Gangam, an AI/ML Engineer based in New York, specializing in designing, deploying, and operationalizing scalable machine learning and Generative AI solutions across enterprise environments.

With nearly 4 years of experience, I have delivered end-to-end pipelines, built LLM-powered applications, and established MLOps practices that improve efficiency, reduce manual work, and enable data-driven decision-making.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Deloitte

March 1, 2025 - Present

Deliver production-ready Generative AI and LLM-based solutions using OpenAI APIs, Hugging Face Transformers, and LangChain, enabling intelligent chat and search experiences that reduced operational support effort by 20%. Develop and operationalize end-to-end machine learning pipelines on Databricks leveraging Python, PySpark, Delta Lake, and MLflow, improving data processing reliability and reducing model training and experimentation time. Implemented Retrieval-Augmented Generation (RAG) workflows with vector databases (Pinecone) to securely integrate enterprise knowledge sources, cutting manual information lookup time by 35% while improving response accuracy. Established foundational MLOps and CI/CD practices using Docker, GitHub Actions, Kubernetes, and Databricks Jobs, enabling automated model packaging, versioning, and deployment, and shortening release cycles under 1 week.

Machine Learning Engineer at Capgemini

January 1, 2021 - December 1, 2023

Designed, trained, and deployed supervised and unsupervised machine learning models using Python, Scikit-Learn, TensorFlow, and XGBoost, improving predictive accuracy by up to 20% across fraud detection, demand forecasting, and risk analytics use cases. Built and optimized batch and near-real-time data pipelines with Apache Airflow, PySpark, and SQL, processing 1M+ records per day, reducing pipeline execution time and ensuring reliable data availability for downstream model training. Deployed and scaled production ML inference services on AWS SageMaker and Kubernetes, supporting 100 K+ API requests per day with sub-200ms latency, high availability, and automated model versioning. Implemented model performance monitoring and observability using MLflow and Prometheus, enabling early detection of data drift and accuracy degradation and reducing production incidents by 30%. Developed NLP-based AI applications and enterprise chatbots using Hugging Face Transformers and OpenAI APIs, reducing