Senior Python & Generative AI Engineer
Resume:
Subhash Akshit Pulipati
*****************@*****.***
Sr Python/Gen AI Engineer
Professional Summary
Python AI / Generative AI Engineer with over 5 years of experience developing machine learning models, intelligent automation systems, and enterprise AI applications. Skilled in Python, Large Language Models (LLMs), Generative AI, Lang Chain, OpenAI APIs, Retrieval Augmented Generation (RAG), and scalable cloud-based ML solutions. Strong experience in building production-ready machine learning pipelines, AI chatbots, NLP applications, and deploying ML models using AWS and Azure. Proven ability to develop data-driven solutions that improve business decision making and operational efficiency across financial and retail industries.
Technical Skills
Programming: Python, SQL, Shell Scripting
Generative AI & LLM: OpenAI API, Lang Chain, Prompt Engineering, Retrieval Augmented Generation (RAG), Vector Databases, Semantic Search, Embeddings, AI Assistants, LLM Application Development
Machine Learning: Scikit-learn, TensorFlow, PyTorch, NLP, Predictive Modelling, Classification, Clustering
Python Libraries: NumPy, Pandas, NLTK, SpaCy, Hugging Face Transformers
Data Engineering: Apache Spark, Kafka, Airflow, ETL Pipelines
Cloud Platforms: AWS (S3, EC2, Lambda, SageMaker, Glue), Azure (Azure ML, Data Factory)
MLOps & DevOps: Docker, Kubernetes, Jenkins, Git, CI/CD, MLFlow
Databases: PostgreSQL, MySQL, MongoDB
Vector Databases: Pinecone, FAISS, Chroma DB
Visualization: Tableau, Power BI, Matplotlib
Professional Experience
Client: Broadridge Financial, Chicago IL
Role: Python Generative AI Engineer (Sep2023 – Present)
Responsibilities:
Designed enterprise Generative AI solutions using Lang Chain and OpenAI APIs to automate financial document analysis and customer query handling.
Built intelligent AI assistants capable of understanding financial data and providing contextual responses.
Implemented Retrieval Augmented Generation (RAG) architecture using Pinecone vector databases to enable knowledge-based financial question answering system.
Developed embedding pipelines that indexed financial knowledge documents for semantic search.
Developed scalable machine learning models using Python and Scikit-learn for fraud detection across high-volume financial transaction datasets.
Improved fraud detection accuracy by implementing advanced feature engineering and model tuning techniques.
Built NLP based text processing systems using SpaCy and Transformer models to analyse banking customer communications.
Implemented entity recognition and sentiment analysis models to improve customer service automation.
Developed REST APIs using Fast API to expose AI model inference services to enterprise banking platforms.
Enabled real-time fraud detection and AI powered recommendations through microservices architecture.
Built automated machine learning pipelines using Apache Airflow to manage model training and deployment workflows.
Scheduled periodic retraining of ML models based on updated financial datasets.
Deployed machine learning models using AWS SageMaker to support scalable AI inference in production environments.
Implemented semantic search systems using vector embeddings to improve financial knowledge discovery.
Developed real-time data streaming pipelines using Kafka and Spark Streaming to process financial transactions.
Implemented prompt engineering techniques to improve accuracy and reliability of LLM responses.
Tuned prompts for financial question answering and document summarization tasks.
Built AI powered chatbots using LLM frameworks to assist customer support teams.
Integrated conversational AI systems into banking support applications.
Implemented containerized ML deployments using Docker and Kubernetes for scalable infrastructure management.
Developed data preprocessing pipelines using Pandas and NumPy for machine learning model training.
Integrated MLFlow for model tracking and performance monitoring across AI systems.
Collaborated with data engineers and business analysts to design scalable AI architectures.
Client: Infosys – India
Role: Python AI / Data Scientist (Jul2021 – Aug2023)
Responsibilities
Developed recommendation systems using collaborative filtering and machine learning algorithms to improve product recommendation accuracy. Increased user engagement and conversion rates on the e-commerce platform.
Built machine learning models using Python and Scikit-learn to forecast product demand and optimize inventory planning. Enabled retail supply chain teams to improve stock availability.
Implemented NLP based sentiment analysis models to analyze large volumes of customer reviews. Provided insights into product quality and customer satisfaction trends.
Developed automated data pipelines using Python and SQL to process large retail transaction datasets. Enabled real-time analytics for e-commerce operations.
Built predictive analytics models to identify high-value customers and purchasing behaviour patterns. Enabled marketing teams to run targeted campaigns.
Implemented clustering algorithms for customer segmentation and personalization strategies. Improved customer engagement through targeted product recommendations.
Built ETL pipelines using Apache Spark to process large-scale retail datasets. Optimized data processing performance across distributed clusters.
Developed RESTful APIs using Python Flask to expose machine learning models for product recommendation systems. Enabled integration with e-commerce web applications.
Built sales forecasting models using regression algorithms to predict product demand trends. Improved retail inventory planning and logistics.
Developed dashboards using Tableau to visualize retail analytics insights. Provided real-time reporting for business stakeholders.
Implemented fraud detection models to identify suspicious online purchase transactions. Reduced financial losses due to fraudulent activities.
Developed automated data validation pipelines to maintain high data quality standards. Ensured accurate analytics and reporting.
Built Python scripts to automate daily retail analytics reporting tasks. Reduced manual workload for business intelligence teams.
Applied hyperparameter tuning techniques to optimize machine learning model performance. Improved predictive accuracy for retail analytics models.
Collaborated with product managers and marketing teams to implement AI driven personalization strategies. Delivered data science solutions aligned with business goals.
Certifications:
AWS Certified Machine Learning Specialty
Microsoft Azure AI Engineer Associate
Python for Data Science Certification
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