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I’m an AI/ML engineer with about 3.5+ years of experience building scalable machine learning and generative AI systems, with a strong focus on production-grade reliability. I specialize in LLM-powered applications, Retrieval-Augmented Generation (RAG), and AI infrastructure, and I enjoy turning models into dependable services that improve performance, latency, and response quality. Across my roles, I’ve designed and deployed end-to-end systems—from REST APIs handling high request volumes to MLOps pipelines for monitoring, evaluation, CI/CD, and drift detection. I’m comfortable with cloud platforms (AWS and GCP), streaming and data engineering stacks (Airflow, Kafka, Spark), and modern deployment tooling (Docker, Kubernetes, MLflow), and I thrive on measurable impact in real enterprise environments.…I’m an AI/ML engineer with about 3.5+ years of experience building scalable machine learning and generative AI systems, with a strong focus on production-grade reliability. I specialize in LLM-powered applications, Retrieval-Augmented Generation (RAG), and AI infrastructure, and I enjoy turning models into dependable services that improve performance, latency, and response quality. Across my roles, I’ve designed and deployed end-to-end systems—from REST APIs handling high request volumes to MLOps pipelines for monitoring, evaluation, CI/CD, and drift detection. I’m comfortable with cloud platforms (AWS and GCP), streaming and data engineering stacks (Airflow, Kafka, Spark), and modern deployment tooling (Docker, Kubernetes, MLflow), and I thrive on measurable impact in real enterprise environments.

Sree D

AI Engineer, Data Scientist, Data Engineer, +3

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I’m an AI/ML engineer with about 3.5+ years of experience building scalable machine learning and generative AI systems, with a strong focus on production-grade reliability. I specialize in LLM-powered applications, Retrieval-Augmented Generation (RAG), and AI infrastructure, and I enjoy turning models into dependable services that improve performance, latency, and response quality. Across my roles, I’ve designed and deployed end-to-end systems—from REST APIs handling high request volumes to MLOps pipelines for monitoring, evaluation, CI/CD, and drift detection. I’m comfortable with cloud platforms (AWS and GCP), streaming and data engineering stacks (Airflow, Kafka, Spark), and modern deployment tooling (Docker, Kubernetes, MLflow), and I thrive on measurable impact in real enterprise environments.…I’m an AI/ML engineer with about 3.5+ years of experience building scalable machine learning and generative AI systems, with a strong focus on production-grade reliability. I specialize in LLM-powered applications, Retrieval-Augmented Generation (RAG), and AI infrastructure, and I enjoy turning models into dependable services that improve performance, latency, and response quality. Across my roles, I’ve designed and deployed end-to-end systems—from REST APIs handling high request volumes to MLOps pipelines for monitoring, evaluation, CI/CD, and drift detection. I’m comfortable with cloud platforms (AWS and GCP), streaming and data engineering stacks (Airflow, Kafka, Spark), and modern deployment tooling (Docker, Kubernetes, MLflow), and I thrive on measurable impact in real enterprise environments.

Available to hire

I’m an AI/ML engineer with about 3.5+ years of experience building scalable machine learning and generative AI systems, with a strong focus on production-grade reliability. I specialize in LLM-powered applications, Retrieval-Augmented Generation (RAG), and AI infrastructure, and I enjoy turning models into dependable services that improve performance, latency, and response quality.

Across my roles, I’ve designed and deployed end-to-end systems—from REST APIs handling high request volumes to MLOps pipelines for monitoring, evaluation, CI/CD, and drift detection. I’m comfortable with cloud platforms (AWS and GCP), streaming and data engineering stacks (Airflow, Kafka, Spark), and modern deployment tooling (Docker, Kubernetes, MLflow), and I thrive on measurable impact in real enterprise environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Language

Work Experience

AI/ML Engineer at Citigroup

July 1, 2025 - Present

Built scalable AI back-end systems using Python and FastAPI to support enterprise generative AI workflows, improving processing efficiency by 30% and enabling high-throughput applications across distributed financial environments. Designed and deployed RAG pipelines using LangChain and ChromaDB on GCP, reducing query latency by 25% while improving response relevance for production-grade virtual assistant capabilities. Developed secure REST APIs supporting over 50K daily requests by integrating LLM-driven services into risk analytics and payment systems, significantly improving throughput, reliability, and real-time decision-making. Containerized and deployed ML microservices with Docker and Kubernetes, achieving 99.5% uptime, accelerating deployment cycles, and improving scalability for orchestration across distributed cloud environments.

AI/ML Engineer at Citi Group

July 1, 2025 - Present

Built scalable AI back-end systems using Python and FastAPI to power enterprise generative AI workflows, improving processing efficiency by 30% and supporting high-throughput applications across distributed financial system environments. Designed and deployed RAG pipelines using LangChain and ChromaDB, reducing query latency by 25% while improving response relevance and enabling production-grade virtual assistant capabilities on GCP infrastructure. Developed secure REST APIs handling over 50K daily requests, integrating LLM-driven services into risk analytics and payments systems to improve throughput, reliability, and real-time decision-making across enterprise platforms. Containerized and deployed ML microservices using Docker and Kubernetes, achieving 99.5% uptime while accelerating deployment cycles, improving scalability, and enabling efficient orchestration across distributed cloud environments.

Data Scientist at Draxo Tech

November 1, 2022 - July 1, 2024

Designed and implemented scalable data processing frameworks for context-aware document analysis, improving retrieval efficiency and enabling intelligent search capabilities over large enterprise datasets. Optimized machine learning models using Scikit-learn and XGBoost, improving forecasting accuracy by 20% to support data-driven decision-making and predictive modeling in business-critical analytics workflows. Deployed machine learning models using Docker and AWS SageMaker, integrating MLflow for experiment tracking and version control, streamlining model lifecycle management, and improving reproducibility across production machine learning systems. Built scalable SQL-based data pipelines for large datasets, reducing preprocessing time by 30%, improving data quality, and enabling efficient downstream analytics and model training across enterprise workflows.

Data Engineer

September 1, 2021 - November 1, 2022

Engineered scalable data pipelines using Apache Airflow and PySpark, improving data ingestion efficiency by 35% and enabling reliable processing of large-scale datasets for advanced analytics and machine learning workflows. Optimized relational databases with PostgreSQL and SQL tuning techniques, improving query performance and enabling efficient data retrieval while leveraging AWS S3 and BigQuery for scalable data storage solutions. Developed real-time streaming pipelines using Apache Kafka, enabling high-throughput data ingestion and ensuring 99.5% data availability for real-time analytics and decision-making systems across distributed enterprise environments.