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AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable Machine Learning and Generative AI solutions across enterprise environments. Skilled in Python, SQL, LLMs, RAG, LangChain, and MLOps, delivering measurable improvements in accuracy, automation, and operational efficiency. Proven in building production-ready AI applications and optimizing data pipelines with AWS, Azure, GCP, Docker, MLflow, and FastAPI, while translating complex business requirements into high-impact solutions and reliable model deployment.…AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable Machine Learning and Generative AI solutions across enterprise environments. Skilled in Python, SQL, LLMs, RAG, LangChain, and MLOps, delivering measurable improvements in accuracy, automation, and operational efficiency. Proven in building production-ready AI applications and optimizing data pipelines with AWS, Azure, GCP, Docker, MLflow, and FastAPI, while translating complex business requirements into high-impact solutions and reliable model deployment.

Shardul Chavan

AI Engineer, Data Scientist, Programmer, +4

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AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable Machine Learning and Generative AI solutions across enterprise environments. Skilled in Python, SQL, LLMs, RAG, LangChain, and MLOps, delivering measurable improvements in accuracy, automation, and operational efficiency. Proven in building production-ready AI applications and optimizing data pipelines with AWS, Azure, GCP, Docker, MLflow, and FastAPI, while translating complex business requirements into high-impact solutions and reliable model deployment.…AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable Machine Learning and Generative AI solutions across enterprise environments. Skilled in Python, SQL, LLMs, RAG, LangChain, and MLOps, delivering measurable improvements in accuracy, automation, and operational efficiency. Proven in building production-ready AI applications and optimizing data pipelines with AWS, Azure, GCP, Docker, MLflow, and FastAPI, while translating complex business requirements into high-impact solutions and reliable model deployment.

Available to hire

AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable Machine Learning and Generative AI solutions across enterprise environments. Skilled in Python, SQL, LLMs, RAG, LangChain, and MLOps, delivering measurable improvements in accuracy, automation, and operational efficiency.

Proven in building production-ready AI applications and optimizing data pipelines with AWS, Azure, GCP, Docker, MLflow, and FastAPI, while translating complex business requirements into high-impact solutions and reliable model deployment.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

AI/ML Engineer at DXC Technology

January 1, 2025 - Present

Architected scalable AI/ML solutions using Python, FastAPI, and AWS; improved prediction accuracy by 21% and reduced inference latency by 38%. Built RAG pipelines with LangChain, OpenAI API, Hugging Face Transformers, and vector databases, increasing response relevance by 34% and reducing hallucinations by 29%. Optimized supervised and ensemble models via feature engineering, hyperparameter tuning, and MLflow tracking, improving precision by 18% and reducing model development cycles by 32%. Automated end-to-end MLOps workflows using Docker, CI/CD, GitHub, and FastAPI, increasing deployment frequency by 45% while maintaining 99.8% availability. Integrated SQL/Snowflake/ETL and Databricks pipelines processing 15M+ records monthly, improving data quality by 30% and reducing reporting delays by 40%. Led XAI, monitoring, and evaluation initiatives to reduce drift incidents by 31% and increase stakeholder confidence by 27%.

ML Engineer at Virtual Infotech Solution

March 1, 2021 - August 31, 2022

Developed predictive ML solutions using Python, scikit-learn, and SQL, improving classification accuracy by 19%. Built robust ETL pipelines integrating structured and semi-structured datasets, reducing manual preparation by 48% and improving consistency by 26%. Performed EDA, hypothesis testing, and feature engineering to uncover insights that improved model performance by 17%. Implemented deep learning models for forecasting/pattern recognition, improving reliability by 22% and reducing false positives by 20%. Delivered REST API-based AI services, decreasing response times by 35% and improving client satisfaction by 18%. Validated models using cross-validation and A/B testing, improving deployment readiness by 24% while minimizing production defects.