AI/ML Engineer - RAG, LLMs, and MLOps Specialist

Anirudh Reddy

AI/ML Engineer +1-940-***-**** ****************@*****.***

SUMMARY:

Data Scientist and Generative AI / ML Engineer with hands-on experience in designing, developing, and deploying scalable machine learning models, LLM applications, and AI solutions across cloud environments. Strong background in supervised and unsupervised learning, NLP, computer vision, deep learning, and MLOps. Proficient in leveraging Gen AI frameworks, transformer models, and vector databases to build RAG systems, chatbots, and LLM fine-tuning pipelines. Experienced in data preprocessing, model training, A/B testing, prompt engineering, and integrating AI services with REST APIs. Demonstrated ability to improve model performance, reduce processing time, and increase data pipeline efficiency. SKILLS:

Languages & Frameworks: Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, OpenAI, Scikit-learn, FastAPI, Flask.

Machine Learning & Gen AI: LLMs, RAG, Fine-tuning, Prompt Engineering, NLP, Computer Vision, Recommendation Systems, Chatbots.

Data & Storage: SQL, Pandas, NumPy, Spark, Hadoop, Hive, Delta Lake, Snowflake, BigQuery

MLOps & Deployment: Docker, Kubernetes, MLflow, SageMaker, Airflow, Git, CI/CD, FastAPI, REST APIs

Vector DBs & Tools: FAISS, Pinecone, Weaviate, ChromaDB, Elasticsearch, Milvus

Cloud Platforms: AWS (S3, EC2, Lambda, SageMaker), GCP, Azure

Visualization & Analysis: Matplotlib, Seaborn, Plotly, Tableau, Power BI EDUCATION:

Master of Science in Computer Science University of North Texas, Denton, Texas May 2024 CERTIFICATION

AWS Solutions Architect – Associate

PROFESSIONAL EXPERIENCE:

AI/ML Engineer OpenAI, USA June 2023 - Present

Developed Retrieval-Augmented Generation (RAG) systems using OpenAI, LangChain, and FAISS, improving response accuracy.

Built LLM-based internal chatbot using GPT-4, Pinecone, and LangChain, reducing customer service query time.

Fine-tuned open-source models (LLaMA2, Falcon) using domain-specific datasets and frameworks like Hugging Face and NLTK, increasing model accuracy.

Deployed scalable AI models using Docker, Kubernetes, and FastAPI, reducing inference latency.

Automated model training, evaluation, and tracking using MLflow and Airflow pipelines, boosting workflow efficiency.

Engineered prompts for LLMs such as GPT-4, Claude, and Gemini to optimize QA and summarization tasks, improving output quality.

Applied advanced NLP techniques using NLTK, Transformers, and LangChain for tasks like entity recognition, summarization, and classification.

Performed A/B testing and model performance evaluation using SQL and Power BI to assess feature enhancements and drive adoption metrics.

Machine Learning Engineer Zensar Technologies, India Jan 2020 - Jul 2022

Developed supervised ML models for demand forecasting and personalized recommendations using Scikit-learn, LightGBM, and XGBoost, improving accuracy.

Built NLP pipelines for text classification, named entity recognition, and sentiment analysis using BERT, spaCy, and NLTK, increasing F1-score.

Created REST APIs with Flask and FastAPI to serve ML models in real time, reducing inference latency.

Implemented computer vision models with TensorFlow and OpenCV for defect detection.

Optimized data pipelines using Apache Spark and Airflow, increasing data throughput and pipeline uptime.

Used AWS SageMaker for end-to-end ML workflows, reducing model deployment and cloud costs.

Built interactive dashboards with Tableau, Seaborn, and Plotly to visualize model performance and business insights.

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