AI/ML Engineer with Generative & RAG Expertise

AI/ML Engineer

Linkedin : linkedin.com/in/akhil-v-67464a21a

Summary:

AI/ML Engineer with 5+ years of hands-on experience designing, developing, and deploying production-grade machine learning and generative AI solutions across healthcare, finance, and enterprise domains. Currently pursuing a PhD in Artificial Intelligence. Strong expertise in Python, deep learning, NLP, and generative AI frameworks including TensorFlow, PyTorch, Hugging Face, and LangChain. Proven experience building agentic AI workflows, Retrieval-Augmented Generation (RAG) pipelines, and integrating vector databases for enterprise search and automation use cases. Experienced in deploying scalable AI solutions on Azure and AWS using MLOps and CI/CD best practices. Adept at translating complex business requirements into reliable, high-impact AI systems and collaborating effectively across cross-functional teams. Education:

University of the Cumberlands, Williamsburg, KY Ph.D. in Information Technology (AI) Expected August 2028 Northwest Missouri State University, USA Master in Computer Science May 2023 Marri Laxman Reddy Institute Of Technology, India Bachelor of Technology in Computer Science May 2021 Languages Python, SQL

ML & AI Frameworks Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Transformers, Copilot Studio Generative AI OpenAI API, GPT, LangChain

Model Tuning & Training Hugging Face, Prompt Engineering Data Processing pandas, NumPy, spaCy, NLTK, OpenCV Deployment & Serving FastAPI, Flask, Docker, Kubernetes Cloud Platforms Azure Machine Learning, Azure Blob Storage, AWS S3, Google Cloud Storage Vector Search / RAG FAISS, Pinecone

Databases SQL, PostgreSQL, MongoDB, Redis

DevOps & MLOps Git, GitHub Actions, Docker, CI/CD

Visualization matplotlib, seaborn, Plotly, Power BI Other Tools Jupyter, VS Code, Azure Notebooks, Google Colab Responsibilities:

● Built agentic AI systems including multi-agent workflows, tool-using LLMs, and stateful memory mechanisms to enable autonomous decision-making.

● Designed and deployed LLM-based applications using frameworks such as LangChain, CrewAI, and Semantic Kernel, enabling intelligent agent workflows, autonomous task handling, and decision-making systems.

● Built Retrieval-Augmented Generation (RAG) pipelines using Vector Databases like FAISS and Pinecone, enhancing enterprise search and document summarization use cases.

● Fine-tuned and evaluated open-source LLMs (e.g., LLaMA, Mistral, Falcon) for domain-specific applications, improving contextual accuracy and reducing hallucinations.

● Engineered multi-agent orchestration with tools like AutoGPT and ReAct-based patterns to simulate collaborative task resolution across systems.

● Applied advanced prompt engineering strategies and dynamic CoT reasoning to optimize LLM outputs for consistency, alignment, and task-specific accuracy.

● Integrated GenAI solutions into production using Azure ML, MLflow, Databricks, and Power Platform, with full- Akhil Vanaparthi

Mail: ******************@*****.***

Phone: +1-737-***-****

Technical Skills

Client: Blue Shield of California

Role: Gen AI Engineer June 2023 – Present

cycle pipeline automation and monitoring.

● Built and customized Copilot agents using Microsoft Copilot Studio, integrating with internal systems and Power Platform tools (Power Automate, Power Apps) for intelligent business process automation.

● Created personalized conversational flows in Copilot Studio, leveraging custom prompts, connectors, and plugin integrations to assist users in HR, finance, and operations workflows.

● Partnered with product and engineering teams to identify and implement impactful GenAI use cases across enterprise domains such as support automation, employee onboarding, and knowledge retrieval. Environment: AI models, GPT, BERT, model architectures, hyperparameters, RAG, SQL, Python, data curation, data transformation, training pipelines, inference pipelines, production environments, prompt engineering, GPT-4, context management, conversational prompts, chain-of-thought prompting, zero-shot learning, few-shot learning, cloud platforms (Azure), deployment, scalability, enterprise architectures, prompt libraries. Responsibilities:

● Designed and deployed end-to-end machine learning solutions using Python, Scikit-learn, TensorFlow, and PyTorch for classification, forecasting, recommendation, and anomaly detection tasks.

● Built NLP pipelines using spaCy, NLTK, and Hugging Face Transformers for tasks such as sentiment analysis, document classification, and named entity recognition (NER).

● Engineered features and preprocessed large-scale datasets using pandas, NumPy, and SQL, optimizing model accuracy and reducing data preparation time.

● Deployed ML models to production via REST APIs using Flask and FastAPI, containerized with Docker, and orchestrated with Airflow and Kubernetes.

● Implemented MLOps best practices including model versioning, CI/CD pipelines, and real-time monitoring using MLflow, Prometheus, and custom AWS CloudWatch metrics.

● Built and maintained automated retraining pipelines triggered by data drift using AWS Lambda, Step Functions, and S3 versioning.

● Developed scalable machine learning workflows using AWS SageMaker, EC2, S3, and Glue, optimizing cost and performance for high-volume data processing.

● Created interactive dashboards and reports using Power BI to present model insights to business stakeholders and leadership.

Environment: AWS, Python, TensorFlow, PyTorch, Scikit-Learn, NLP (spaCy, Hugging Face, BERT), LangChain, Generative AI, RAG, Apache Spark, Databricks, Azure, FastAPI, Flask, Django, CI/CD, Kafka Streams, AWS CloudWatch, MLOps.

Responsibilities:

● Developed and deployed ML models for classification and regression tasks using Python, Scikit-learn, and XGBoost, improving predictive accuracy by 20%.

● Built and fine-tuned NLP pipelines for document categorization and sentiment analysis using spaCy and Hugging Face Transformers.

● Designed feature engineering workflows to process structured and semi-structured data using pandas, NumPy, and SQL.

● Automated model training and evaluation pipelines using MLflow for experiment tracking and Docker for consistent deployment environments.

● Collaborated with data scientists and backend engineers to deploy models via REST APIs using Flask into a production AWS environment (EC2, S3, SageMaker).

● Monitored model performance post-deployment using AWS CloudWatch, implementing retraining triggers based on data drift and accuracy thresholds.

● Contributed to the development of internal tools for dataset versioning and model explainability using SHAP and LIME.

● Communicated model performance and insights to stakeholders through interactive Power BI dashboards. Environment: Machine Learning, TensorFlow, PyTorch, Scikit-Learn, Azure ML, NLP, spaCy, Hugging Face Transformers, Apache Spark, Hadoop, Databricks, CI/CD, Azure DevOps, Docker, Kubernetes,, Django REST framework, Pandas, NumPy, SQL.

Client: Ally Financial

Role: AI/ML Engineer September 2021 - May 2023

Full Time: Infosys, India

Role: ML Engineer June 2020 - July 2021

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