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I am a Machine Learning & AI Engineer with 3+ years of experience building end-to-end ML systems, scalable inference services, and cloud-native deployment pipelines. I specialize in deep learning, NLP, LLMs, RAG pipelines, vector search, and Generative AI with a strong focus on production reliability. I have hands-on experience with MLOps automation using Kubeflow, MLflow, and CI/CD to optimize training workflows, reduce retraining cycles, and enhance system observability. I am known for delivering high-impact ML solutions that improve accuracy, scalability, and business performance.…I am a Machine Learning & AI Engineer with 3+ years of experience building end-to-end ML systems, scalable inference services, and cloud-native deployment pipelines. I specialize in deep learning, NLP, LLMs, RAG pipelines, vector search, and Generative AI with a strong focus on production reliability. I have hands-on experience with MLOps automation using Kubeflow, MLflow, and CI/CD to optimize training workflows, reduce retraining cycles, and enhance system observability. I am known for delivering high-impact ML solutions that improve accuracy, scalability, and business performance.

Yash Sharma

AI Engineer, Data Scientist, Data Analyst, +2

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I am a Machine Learning & AI Engineer with 3+ years of experience building end-to-end ML systems, scalable inference services, and cloud-native deployment pipelines. I specialize in deep learning, NLP, LLMs, RAG pipelines, vector search, and Generative AI with a strong focus on production reliability. I have hands-on experience with MLOps automation using Kubeflow, MLflow, and CI/CD to optimize training workflows, reduce retraining cycles, and enhance system observability. I am known for delivering high-impact ML solutions that improve accuracy, scalability, and business performance.…I am a Machine Learning & AI Engineer with 3+ years of experience building end-to-end ML systems, scalable inference services, and cloud-native deployment pipelines. I specialize in deep learning, NLP, LLMs, RAG pipelines, vector search, and Generative AI with a strong focus on production reliability. I have hands-on experience with MLOps automation using Kubeflow, MLflow, and CI/CD to optimize training workflows, reduce retraining cycles, and enhance system observability. I am known for delivering high-impact ML solutions that improve accuracy, scalability, and business performance.

Available to hire

I am a Machine Learning & AI Engineer with 3+ years of experience building end-to-end ML systems, scalable inference services, and cloud-native deployment pipelines. I specialize in deep learning, NLP, LLMs, RAG pipelines, vector search, and Generative AI with a strong focus on production reliability.

I have hands-on experience with MLOps automation using Kubeflow, MLflow, and CI/CD to optimize training workflows, reduce retraining cycles, and enhance system observability. I am known for delivering high-impact ML solutions that improve accuracy, scalability, and business performance.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Principal Financial

March 1, 2025 - Present

Designed scalable deep-learning pipelines using TensorFlow and Keras, reducing experiment runtime and iteration cycles by 20%. Implemented advanced architectures (VAEs, LSTMs, Transformers) increasing anomaly detection accuracy on financial datasets by 30%. Built LLM-based RAG pipelines with embeddings and vector search for internal knowledge assistants. Managed MLflow experiment tracking and model registries for reproducible runs and lifecycle management. Enhanced MLOps with automated CI/CD, drift monitoring, and containerized deployments on Kubernetes.

Machine Learning Engineer at Insight Global

March 1, 2021 - July 1, 2023

Architected real-time inference APIs using FastAPI and Docker, supporting 1M+ daily predictions with 99.9% uptime. Built fraud detection models using VAEs and LSTMs, increasing anomaly detection accuracy by 30% on transactional data. Developed ETL pipelines and optimized Snowflake/PostgreSQL queries, improving feature readiness and query latency by 30%. Standardized MLOps lifecycle with Kubeflow and MLflow, enabling automated retraining and reducing refresh cycles by 40%. Migrated ML services to microservices using Docker and Kubernetes, improving availability by 20% and lowering compute cost by 15%. Implemented CI/CD automation with Jenkins and GitHub Actions, accelerating deployment frequency and reducing release failures. Integrated Prometheus and Grafana dashboards for latency, drift, and anomaly monitoring across production ML services. Conducted A/B testing and hypothesis-driven experiments to validate model behavior and guide data-driven product decisions.