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AI/ML Engineer with 4+ years of experience building and deploying machine learning, deep learning, and Generative AI solutions in finance and enterprise environments. I focus on production ML pipelines, LLM applications, RAG systems, NLP, and scalable inference services across AWS and GCP. I’m experienced in MLOps workflows including feature engineering, model deployment, monitoring, and experiment tracking (MLflow/Airflow). I enjoy turning complex ML capabilities into reliable, measurable business outcomes—improving accuracy, reducing latency, and accelerating deployment timelines.…AI/ML Engineer with 4+ years of experience building and deploying machine learning, deep learning, and Generative AI solutions in finance and enterprise environments. I focus on production ML pipelines, LLM applications, RAG systems, NLP, and scalable inference services across AWS and GCP. I’m experienced in MLOps workflows including feature engineering, model deployment, monitoring, and experiment tracking (MLflow/Airflow). I enjoy turning complex ML capabilities into reliable, measurable business outcomes—improving accuracy, reducing latency, and accelerating deployment timelines.

Shashank Sama

AI Engineer, Data Scientist, Software Engineer, +2

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AI/ML Engineer with 4+ years of experience building and deploying machine learning, deep learning, and Generative AI solutions in finance and enterprise environments. I focus on production ML pipelines, LLM applications, RAG systems, NLP, and scalable inference services across AWS and GCP. I’m experienced in MLOps workflows including feature engineering, model deployment, monitoring, and experiment tracking (MLflow/Airflow). I enjoy turning complex ML capabilities into reliable, measurable business outcomes—improving accuracy, reducing latency, and accelerating deployment timelines.…AI/ML Engineer with 4+ years of experience building and deploying machine learning, deep learning, and Generative AI solutions in finance and enterprise environments. I focus on production ML pipelines, LLM applications, RAG systems, NLP, and scalable inference services across AWS and GCP. I’m experienced in MLOps workflows including feature engineering, model deployment, monitoring, and experiment tracking (MLflow/Airflow). I enjoy turning complex ML capabilities into reliable, measurable business outcomes—improving accuracy, reducing latency, and accelerating deployment timelines.

Available to hire

AI/ML Engineer with 4+ years of experience building and deploying machine learning, deep learning, and Generative AI solutions in finance and enterprise environments. I focus on production ML pipelines, LLM applications, RAG systems, NLP, and scalable inference services across AWS and GCP.

I’m experienced in MLOps workflows including feature engineering, model deployment, monitoring, and experiment tracking (MLflow/Airflow). I enjoy turning complex ML capabilities into reliable, measurable business outcomes—improving accuracy, reducing latency, and accelerating deployment timelines.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

AI/ML Engineer at JPMorgan Chase & Co.

March 1, 2024 - Present

Engineered machine learning models for financial risk assessment and transaction analytics, improving prediction accuracy by 35% and supporting data-driven decisions across risk and compliance. Built LSTM-based fraud detection with 98% accuracy to identify high-risk transactions and reduce false positives. Fine-tuned transformer models (including BERT) for document classification and text analytics, improving performance by 10%. Designed RAG applications using LangChain, vector search, and enterprise knowledge repositories, reducing retrieval time by 40%. Implemented FastAPI-based inference services and deployed models on AWS SageMaker, EC2, and Lambda, reducing prediction latency by 30%. Optimized feature engineering and hyperparameter tuning with MLflow and improved experiment/evaluation workflows by 25%. Automated ML and data processing pipelines to reduce operational latency by 40% and improve deployment reliability, including agent-based automation components for faster adoption a

AI Engineer at Accenture

February 1, 2021 - July 1, 2022

Designed NLP models using BERT and recurrent neural networks for intent classification and named entity recognition, improving information extraction on customer datasets. Built OCR and document-processing pipelines using OpenCV and retrieval techniques, increasing text extraction accuracy to 98% and reducing manual effort. Improved production inference efficiency by 38% using quantization and pruning while maintaining accuracy. Built predictive models (XGBoost, CNNs, time-series) to support forecasting and decision-making. Deployed ML applications on AWS and implemented monitoring with CloudWatch, reducing issue resolution time by 30% and improving reliability. Developed semantic search apps using embeddings and retrieval to reduce knowledge lookup time by 30%. Contributed to an LLM-powered customer support assistant integrated with enterprise knowledge bases, improving response quality and customer satisfaction by 25%.