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I am Harika Manukonda, an AI/ML Engineer and Data Scientist with around 5 years of experience delivering scalable machine learning, MLOps, Generative AI applications, and LLMOps solutions across McKesson, Airbnb, and Dell Technologies. I thrive on building production-grade AI systems with robust governance and compliance frameworks, and I enjoy collaborating with cross-functional teams to translate complex data into business impact. I am proficient in Python, TensorFlow, PyTorch, and LangChain, with hands-on experience in AWS SageMaker, Azure ML, and Snowflake, and I excel in delivering end-to-end ML pipelines that automate processes and enable data-driven decision making. In my roles I’ve delivered multimodal forecasting, RAG pipelines, and GenAI deployments that improve resilience and efficiency across healthcare and tech environments. I’m actively expanding capabilities in LLMOps, governance, observability, and scalable AI architectures, always balancing innovation with reliability, security, and compliance.…I am Harika Manukonda, an AI/ML Engineer and Data Scientist with around 5 years of experience delivering scalable machine learning, MLOps, Generative AI applications, and LLMOps solutions across McKesson, Airbnb, and Dell Technologies. I thrive on building production-grade AI systems with robust governance and compliance frameworks, and I enjoy collaborating with cross-functional teams to translate complex data into business impact. I am proficient in Python, TensorFlow, PyTorch, and LangChain, with hands-on experience in AWS SageMaker, Azure ML, and Snowflake, and I excel in delivering end-to-end ML pipelines that automate processes and enable data-driven decision making. In my roles I’ve delivered multimodal forecasting, RAG pipelines, and GenAI deployments that improve resilience and efficiency across healthcare and tech environments. I’m actively expanding capabilities in LLMOps, governance, observability, and scalable AI architectures, always balancing innovation with reliability, security, and compliance.

Harika Manukonda

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

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I am Harika Manukonda, an AI/ML Engineer and Data Scientist with around 5 years of experience delivering scalable machine learning, MLOps, Generative AI applications, and LLMOps solutions across McKesson, Airbnb, and Dell Technologies. I thrive on building production-grade AI systems with robust governance and compliance frameworks, and I enjoy collaborating with cross-functional teams to translate complex data into business impact. I am proficient in Python, TensorFlow, PyTorch, and LangChain, with hands-on experience in AWS SageMaker, Azure ML, and Snowflake, and I excel in delivering end-to-end ML pipelines that automate processes and enable data-driven decision making. In my roles I’ve delivered multimodal forecasting, RAG pipelines, and GenAI deployments that improve resilience and efficiency across healthcare and tech environments. I’m actively expanding capabilities in LLMOps, governance, observability, and scalable AI architectures, always balancing innovation with reliability, security, and compliance.…I am Harika Manukonda, an AI/ML Engineer and Data Scientist with around 5 years of experience delivering scalable machine learning, MLOps, Generative AI applications, and LLMOps solutions across McKesson, Airbnb, and Dell Technologies. I thrive on building production-grade AI systems with robust governance and compliance frameworks, and I enjoy collaborating with cross-functional teams to translate complex data into business impact. I am proficient in Python, TensorFlow, PyTorch, and LangChain, with hands-on experience in AWS SageMaker, Azure ML, and Snowflake, and I excel in delivering end-to-end ML pipelines that automate processes and enable data-driven decision making. In my roles I’ve delivered multimodal forecasting, RAG pipelines, and GenAI deployments that improve resilience and efficiency across healthcare and tech environments. I’m actively expanding capabilities in LLMOps, governance, observability, and scalable AI architectures, always balancing innovation with reliability, security, and compliance.

Available to hire

I am Harika Manukonda, an AI/ML Engineer and Data Scientist with around 5 years of experience delivering scalable machine learning, MLOps, Generative AI applications, and LLMOps solutions across McKesson, Airbnb, and Dell Technologies. I thrive on building production-grade AI systems with robust governance and compliance frameworks, and I enjoy collaborating with cross-functional teams to translate complex data into business impact. I am proficient in Python, TensorFlow, PyTorch, and LangChain, with hands-on experience in AWS SageMaker, Azure ML, and Snowflake, and I excel in delivering end-to-end ML pipelines that automate processes and enable data-driven decision making.

In my roles I’ve delivered multimodal forecasting, RAG pipelines, and GenAI deployments that improve resilience and efficiency across healthcare and tech environments. I’m actively expanding capabilities in LLMOps, governance, observability, and scalable AI architectures, always balancing innovation with reliability, security, and compliance.

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Work Experience

AI/ML Engineer / Data Scientist at McKesson

September 1, 2024 - Present

Developed multimodal forecasting systems using Python, TensorFlow, and OpenAI API, training hybrid LLMs and predictive models on 10M+ logistics transactions to cut supply-chain disruption incidents by 2,300 annually across critical healthcare operations. Constructed advanced Retrieval-Augmented Generation (RAG) pipelines with LangChain, Hugging Face, LlamaIndex, and FAISS, integrating domain-tuned medical text embeddings that automated clinical report summarization across 5 enterprise systems, saving 7,800 labor hours yearly. Orchestrated production-grade MLOps and GenAIOps workflows using AWS SageMaker, MLflow, Docker, and Kubernetes, embedding human feedback loops and model governance tracking, ensuring full compliance across 12 deployed AI services within McKesson’s regulated data ecosystem. Integrated LLMOps workflows for managing model lifecycle, compliance, and observability across Generative AI deployments.

AI/ML Engineer at Airbnb

December 1, 2022 - August 1, 2023

Developed large-scale ML data pipelines using Python (Pandas, PySpark) and SQL, automating ingestion from Snowflake and AWS S3, processing 5.3M+ daily transactions for forecasting and anomaly detection models. Trained and optimized machine learning models in TensorFlow, scikit-learn, and XGBoost, achieving measurable gains in demand prediction accuracy and improving operational planning for regional market analytics. Administered automated data preprocessing and feature engineering pipelines with dbt and Airflow, improving model retraining speed and ensuring schema consistency across evolving datasets. Deployed ML inference workflows using Docker, FastAPI, and AWS Lambda, enabling real-time model serving and reducing prediction latency by 35% across live traffic streams. Monitored model performance and data drift through MLflow and Prometheus, maintaining version control, experiment tracking, and reproducibility across all production environments. Collaborated with cross-functional tea

ML Engineer at Dell Technologies

June 1, 2020 - November 1, 2022

Built and automated end-to-end ML pipelines using Azure Data Factory, Python, and SQL, processing 1.5M+ daily transactions to support model training, validation, and continuous performance monitoring. Implemented robust MLOps practices with Azure ML, Docker, and GitHub Actions, automating model retraining and deployment across 4 production clusters, reducing delivery time by 70 hours per month. Engineered advanced feature extraction and transformation workflows using PySpark, Pandas, and NumPy, improving forecasting model precision across 10 global business segments. Crafted and tuned predictive algorithms (ARIMA, XGBoost, Random Forest) for demand forecasting and anomaly detection, enhancing model accuracy and business scheduling efficiency. Integrated cloud and on-prem data systems through Azure Synapse, SQL Server, and REST APIs, ensuring secure model access, experiment reproducibility, and low-latency analytics for ML workloads. Deployed model performance monitoring and drift detec