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I'm Aditya Deshmukh, an AI Engineer with around 7 years of experience delivering GenAI, RAG, agentic AI, machine learning, and data platform solutions across healthcare, insurance, finance, and enterprise domains. I design production-grade GenAI workflows, including retrieval, prompt engineering, context injection, LLM inference, safety layers, and evaluation, using Python, PyTorch, Pandas, and SQL-based data platforms. I enjoy turning complex governance and data-security requirements into robust, scalable architectures that empower enterprise teams. I focus on secure hybrid-cloud deployments, MLOps, and continuous model monitoring with drift detection and automated retraining. I collaborate with cross-functional teams to pilot high-value GenAI use cases, grounding model outputs in internal policies and data, and delivering measurable improvements in accuracy, latency, and safety.…I'm Aditya Deshmukh, an AI Engineer with around 7 years of experience delivering GenAI, RAG, agentic AI, machine learning, and data platform solutions across healthcare, insurance, finance, and enterprise domains. I design production-grade GenAI workflows, including retrieval, prompt engineering, context injection, LLM inference, safety layers, and evaluation, using Python, PyTorch, Pandas, and SQL-based data platforms. I enjoy turning complex governance and data-security requirements into robust, scalable architectures that empower enterprise teams. I focus on secure hybrid-cloud deployments, MLOps, and continuous model monitoring with drift detection and automated retraining. I collaborate with cross-functional teams to pilot high-value GenAI use cases, grounding model outputs in internal policies and data, and delivering measurable improvements in accuracy, latency, and safety.

Aditya Deshmukh

AI Engineer, Data Scientist, Full Stack Developer, +2

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I'm Aditya Deshmukh, an AI Engineer with around 7 years of experience delivering GenAI, RAG, agentic AI, machine learning, and data platform solutions across healthcare, insurance, finance, and enterprise domains. I design production-grade GenAI workflows, including retrieval, prompt engineering, context injection, LLM inference, safety layers, and evaluation, using Python, PyTorch, Pandas, and SQL-based data platforms. I enjoy turning complex governance and data-security requirements into robust, scalable architectures that empower enterprise teams. I focus on secure hybrid-cloud deployments, MLOps, and continuous model monitoring with drift detection and automated retraining. I collaborate with cross-functional teams to pilot high-value GenAI use cases, grounding model outputs in internal policies and data, and delivering measurable improvements in accuracy, latency, and safety.…I'm Aditya Deshmukh, an AI Engineer with around 7 years of experience delivering GenAI, RAG, agentic AI, machine learning, and data platform solutions across healthcare, insurance, finance, and enterprise domains. I design production-grade GenAI workflows, including retrieval, prompt engineering, context injection, LLM inference, safety layers, and evaluation, using Python, PyTorch, Pandas, and SQL-based data platforms. I enjoy turning complex governance and data-security requirements into robust, scalable architectures that empower enterprise teams. I focus on secure hybrid-cloud deployments, MLOps, and continuous model monitoring with drift detection and automated retraining. I collaborate with cross-functional teams to pilot high-value GenAI use cases, grounding model outputs in internal policies and data, and delivering measurable improvements in accuracy, latency, and safety.

Available to hire

I’m Aditya Deshmukh, an AI Engineer with around 7 years of experience delivering GenAI, RAG, agentic AI, machine learning, and data platform solutions across healthcare, insurance, finance, and enterprise domains. I design production-grade GenAI workflows, including retrieval, prompt engineering, context injection, LLM inference, safety layers, and evaluation, using Python, PyTorch, Pandas, and SQL-based data platforms. I enjoy turning complex governance and data-security requirements into robust, scalable architectures that empower enterprise teams.

I focus on secure hybrid-cloud deployments, MLOps, and continuous model monitoring with drift detection and automated retraining. I collaborate with cross-functional teams to pilot high-value GenAI use cases, grounding model outputs in internal policies and data, and delivering measurable improvements in accuracy, latency, and safety.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI Engineer at HeronAI

October 1, 2024 - Present

Gathered stakeholder requirements and defined success metrics; designed RAG architectures with vector databases, retrievers, and prompting strategies; deployed PyTorch models via FastAPI; implemented secure hybrid-cloud deployments on AWS/GCP; integrated LLM tooling controls; built agentic components using LangChain; established CI/CD, monitoring, and retraining pipelines; conducted red-team safety assessments.

AI/ML Engineer at SEPIA Innovation

December 1, 2021 - July 1, 2023

Integrated Snowflake data models and SQL transformations; designed end-to-end RAG pipelines grounding model outputs in internal policies; fine-tuned PyTorch models for NLP and predictive analytics; built microservices and APIs for claims and underwriting workflows; defined secure cloud architectures and implemented MLOps pipelines with automated training and deployment.

Data Scientist at IO Green Solutions

October 1, 2018 - March 1, 2021

Built ETL pipelines, engineered features for risk scoring and time-series analysis; developed ML models for credit-risk prediction; built DL architectures to analyze financial texts and disclosures; integrated predictive scores into analyst platforms; implemented MLOps pipelines and automated retraining.

Data Engineer at JABIL Circuits

July 1, 2017 - July 1, 2018

Built automated data-validation frameworks; implemented batch/incremental data ingestion; cleaned and standardized data; developed reusable transformation components; deployed pipelines via CI/CD and monitoring; ensured data quality and lineage for analytics and governance.