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AI/ML Engineer with 3+ years of experience building and deploying machine learning systems across networking, fintech, and IT consulting. Experienced in the end-to-end ML lifecycle including data engineering, model development, deployment, and monitoring. Hands-on with LLMs and Generative AI, NLP, computer vision, and MLOps. Strong track record of reducing latency, improving detection accuracy, and integrating AI into production environments using TensorFlow, PyTorch, Hugging Face, LangChain, and cloud platforms like AWS SageMaker, Azure ML, and GCP Vertex AI.…AI/ML Engineer with 3+ years of experience building and deploying machine learning systems across networking, fintech, and IT consulting. Experienced in the end-to-end ML lifecycle including data engineering, model development, deployment, and monitoring. Hands-on with LLMs and Generative AI, NLP, computer vision, and MLOps. Strong track record of reducing latency, improving detection accuracy, and integrating AI into production environments using TensorFlow, PyTorch, Hugging Face, LangChain, and cloud platforms like AWS SageMaker, Azure ML, and GCP Vertex AI.

LAKSHMI TILAK

Data Scientist, AI Engineer, Full Stack Developer, +5

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AI/ML Engineer with 3+ years of experience building and deploying machine learning systems across networking, fintech, and IT consulting. Experienced in the end-to-end ML lifecycle including data engineering, model development, deployment, and monitoring. Hands-on with LLMs and Generative AI, NLP, computer vision, and MLOps. Strong track record of reducing latency, improving detection accuracy, and integrating AI into production environments using TensorFlow, PyTorch, Hugging Face, LangChain, and cloud platforms like AWS SageMaker, Azure ML, and GCP Vertex AI.…AI/ML Engineer with 3+ years of experience building and deploying machine learning systems across networking, fintech, and IT consulting. Experienced in the end-to-end ML lifecycle including data engineering, model development, deployment, and monitoring. Hands-on with LLMs and Generative AI, NLP, computer vision, and MLOps. Strong track record of reducing latency, improving detection accuracy, and integrating AI into production environments using TensorFlow, PyTorch, Hugging Face, LangChain, and cloud platforms like AWS SageMaker, Azure ML, and GCP Vertex AI.

Available to hire

AI/ML Engineer with 3+ years of experience building and deploying machine learning systems across networking, fintech, and IT consulting. Experienced in the end-to-end ML lifecycle including data engineering, model development, deployment, and monitoring.

Hands-on with LLMs and Generative AI, NLP, computer vision, and MLOps. Strong track record of reducing latency, improving detection accuracy, and integrating AI into production environments using TensorFlow, PyTorch, Hugging Face, LangChain, and cloud platforms like AWS SageMaker, Azure ML, and GCP Vertex AI.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Advanced

Work Experience

AI/ML Engineer at Juniper Networks

May 1, 2025 - Present

Designed anomaly detection models using deep learning on network telemetry, improving true-positive detection by 32% and reducing false positives by 18%. Implemented Transformer-based NLP (BERT, GPT) for automated log parsing, reducing mean-time-to-detect incidents by 15% and improving escalation accuracy. Built streaming feature pipelines with Kafka and Spark for real-time processing of 5M+ daily network events. Deployed ML models on Kubernetes with Docker and MLflow, reducing inference latency from 280ms to 110ms and achieving 99.95% uptime. Developed a RAG assistant with LangChain and vector databases, cutting troubleshooting time by 22%. Integrated AI intrusion detection with Splunk and QRadar to enhance SOC monitoring coverage and efficiency.

Data Scientist at Juniper Networks

May 1, 2025 - Present

Designed anomaly detection models using deep learning on network telemetry data, improving true-positive detection rates by 32% and reducing false positives. Implemented Transformer-based NLP solutions (BERT, GPT) for automated log parsing, reducing mean-time-to-detect incidents. Built streaming feature pipelines with Kafka and Spark for real-time analysis of over 5 million daily network events. Deployed ML models on Kubernetes with Docker and MLflow, reducing inference latency from 280ms to 110ms and increasing service uptime. Developed a retrieval-augmented generation (RAG) assistant with LangChain and vector databases, cutting troubleshooting time for support engineers. Integrated AI-based intrusion detection with Splunk and QRadar, enhancing security monitoring coverage and SOC efficiency.

Machine Learning Engineer at Datamatics

July 1, 2021 - July 1, 2023

Delivered fraud detection models for financial transactions using XGBoost and ensemble methods, achieving 96% precision and reducing false investigations. Developed and deployed recommendation systems on AWS SageMaker, driving a 20% increase in customer engagement and 8% higher conversions. Applied CNN and Transformer-based OCR models for document automation, reducing manual data entry effort by 45%. Automated ML workflows with Airflow and Spark on Databricks, reducing ETL and feature engineering time by 20%. Established CI/CD pipelines with GitHub Actions and MLflow, enabling safe weekly releases with blue-green deployments. Built real-time monitoring dashboards with Prometheus and Grafana for drift detection and SLA tracking, reducing downtime and data quality issues.

Data Scientist / AI Engineer at 3i Infotech

January 1, 2019 - June 1, 2020

Built predictive models for customer segmentation and churn reduction, increasing campaign ROI by 10%. Implemented NLP sentiment and topic models with Word2Vec and LSTM to analyze customer feedback, reducing recurring complaints. Automated ETL processes using Python and SQL with data validation checks, improving reporting efficiency by 20%. Designed interactive Tableau dashboards to track ML model performance and business KPIs, improving executive decision-making speed. Deployed AI models in production with Docker and versioned training environments, streamlining delivery to IT teams. Established feature reuse across multiple projects with a centralized feature store, improving productivity and consistency. Monitored schema drift and implemented safeguards against null-value issues, reducing data-related outages.