Summary
Overview
Work History
Education
Skills
Timeline
Generic

Charishma Polineni

Summary

Generative AI Engineer with around 9 years of experience designing, developing, and deploying AI-driven solutions using large language models (LLMs), multimodal AI, and deep learning architectures. Proven track record of building end-to-end AI pipelines, optimizing model performance, and integrating Gen AI solutions into enterprise systems. Skilled in retrieval-augmented generation (RAG), prompt engineering, and vector database integration to build intelligent, context-aware AI solutions. Hands-on expertise with OpenAI, Hugging Face Transformers, LangChain, and cloud AI services (AWS SageMaker, Azure AI, Vertex AI). Adept at designing end-to-end AI pipelines, from data preparation and model training to production deployment and performance monitoring. Track record of reducing operational costs and accelerating delivery timelines through AI-driven automation and creative content generation. Strong collaboration skills, working with cross-functional teams to integrate AI capabilities into products and workflows seamlessly. Designed and deployed multi-agent AI systems for complex problem-solving using frameworks like LangChain Agents and Auto-GPT. Experienced in building domain-specific knowledge bases and integrating them with LLMs for context-rich responses. Developed multi-turn conversational AI with memory and personalization for customer engagement platforms. Implemented few-shot and zero-shot learning techniques to improve model adaptability without extensive retraining. Integrated vector search technologies (Pinecone, Weaviate, Chroma) with LLM pipelines for lightning-fast context retrieval. Created custom embeddings and semantic similarity algorithms to enhance AI reasoning and search capabilities. Experienced in cost optimization for LLM-based services, reducing inference costs through quantization and model distillation. Collaborated with data science and product teams to translate AI research into user-facing, revenue-generating solutions. Passionate about advancing Gen AI technologies to enhance personalization, automation, and decision-making at scale. Experienced in fine-tuning and optimizing LLMs (GPT, LLaMA, Mistral) for domain-specific applications, improving accuracy and relevance. Built custom AI-powered chatbots and virtual assistants leveraging RAG, embeddings, and semantic search for enhanced user interaction. Skilled in model evaluation, A/B testing, and implementing feedback loops for continuous AI performance improvement. Implemented scalable AI architectures using Docker, Kubernetes, and CI/CD pipelines for reliable production deployments. Leveraged Whisper and speech models for building real-time transcription, summarization, and voice-based applications.

Overview

9
9
years of professional experience

Work History

Generative AI Engineer

Citizens Financial Group
06.2024 - Current
  • Developed AI-powered document summarization tool using LangChain, enabling automatic extraction of key insights from large PDF and text corpora.
  • Built multimodal AI pipelines integrating Stable Diffusion, Whisper, and TTS models to deliver text-to-image, speech-to-text, and text-to-speech capabilities.
  • Created custom embeddings for semantic search applications, enabling high-speed, context-aware information retrieval in enterprise knowledge systems.
  • Integrated Generative AI APIs (OpenAI, Hugging Face, Vertex AI) into enterprise applications, enhancing personalization and automation workflows.
  • Optimized inference costs for LLM-based applications by implementing model quantization, caching strategies, and distillation techniques.
  • Created and deployed multimodal AI workflows that integrated text, audio, and image generation, enabling cross-format automation for marketing, customer service, and training platforms.
  • Built enterprise knowledge assistants by integrating vector databases, embeddings, and LLM APIs, enabling quick and accurate retrieval of relevant information from large document repositories.
  • Implemented automated prompt engineering strategies, including few-shot, chain-of-thought, and instruction-based approaches, to optimize response accuracy and reduce hallucinations in AI outputs.
  • Developed AI-powered document processing tools that extract, summarize, and classify information from complex legal, financial, and technical documents.
  • Integrated AI capabilities into existing enterprise platforms through secure API development, ensuring seamless interoperability between AI models and legacy systems.
  • Designed cloud-native AI pipelines using AWS SageMaker, Azure AI, and Google Vertex AI, enabling scalable, fault-tolerant deployment of generative models.
  • Conducted research and experimentation on model compression, quantization, and distillation techniques to optimize performance and reduce infrastructure costs.
  • Developed intelligent content generation platforms using LLMs and Stable Diffusion, enabling automated creation of blogs, marketing copy, and creative assets aligned with brand guidelines.
  • Engineered AI-driven chat assistants capable of maintaining multi-turn context, personalized responses, and memory retention for improved user engagement in customer-facing applications.
  • Built retrieval-based AI solutions by integrating semantic search, embeddings, and vector databases, allowing systems to dynamically pull and use relevant information during interactions.
  • Designed multimodal generative AI systems combining speech recognition, natural language understanding, and image synthesis for next-generation user experiences.
  • Created automated pipelines for continuous model evaluation, incorporating human feedback loops and structured test cases to refine LLM outputs over time.
  • Collaborated with product managers, data scientists, and UX designers to translate business requirements into AI-driven product features that enhance user experience and engagement.
  • Collaborated with cross-functional teams to design AI solutions aligned with business goals, ensuring seamless integration into existing platforms.
  • Implemented model monitoring and drift detection to maintain accuracy and performance in production environments.
  • Implemented model evaluation and monitoring dashboards to track performance drift and optimize retraining cycles.

AI/ML Engineer

Chevron Corporation
07.2022 - 05.2024
  • Designed and implemented end-to-end machine learning pipelines, including data ingestion, preprocessing, feature engineering, model training, and deployment into production environments.
  • Built predictive analytics models using supervised and unsupervised learning algorithms to solve classification, regression, and clustering problems for diverse business use cases.
  • Developed and optimized deep learning architectures in TensorFlow and PyTorch, applying techniques like transfer learning, regularization, and hyperparameter tuning.
  • Created scalable, cloud-native ML workflows using AWS SageMaker, Azure Machine Learning, and Google Vertex AI for efficient experimentation and deployment.
  • Collaborated with data engineering teams to design robust ETL pipelines for cleaning, transforming, and storing large datasets in data lakes and warehouses.
  • Integrated AI capabilities into web and mobile applications through RESTful APIs and microservices, ensuring low-latency inference and high availability.
  • Implemented model monitoring and drift detection mechanisms to maintain accuracy and reliability in live production environments.
  • Designed computer vision models for image classification, object detection, and segmentation tasks using frameworks such as PyTorch, TensorFlow, and OpenCV.
  • Built NLP solutions for sentiment analysis, named entity recognition, and document summarization, leveraging Hugging Face Transformers and spaCy.
  • Optimized ML inference pipelines through model quantization, pruning, and batching strategies to improve latency and reduce computational costs.
  • Created synthetic datasets to address class imbalance and data scarcity, improving model robustness in real-world scenarios.
  • Developed and maintained data preprocessing scripts for handling missing values, outliers, and categorical encoding to improve model performance.
  • Engineered time-series forecasting models using ARIMA, Prophet, and LSTM networks for demand planning and operational optimization.
  • Implemented active learning workflows to iteratively retrain models with high-value samples, reducing annotation costs and improving accuracy.
  • Built real-time analytics dashboards integrating ML predictions, enabling stakeholders to monitor model outcomes and KPIs interactively.
  • Deployed AI solutions with Kubernetes-based orchestration, ensuring scalability and high availability in production workloads.
  • Conducted research and experimentation with state-of-the-art ML algorithms, applying cutting-edge techniques to improve performance and adaptability.
  • Developed automated testing and CI/CD pipelines for machine learning models, ensuring consistent deployment and rollback capabilities.
  • Worked with cross-functional teams, including product managers and data scientists, to translate business goals into technical AI/ML solutions.
  • Leveraged big data frameworks like Apache Spark and Hadoop for distributed data processing and large-scale model training.

Data Science

Optum
03.2020 - 06.2022
  • Designed and executed end-to-end data science workflows, including data collection, cleaning, transformation, exploratory analysis, and predictive modeling.
  • Built statistical and machine learning models for classification, regression, clustering, and forecasting, translating business problems into analytical solutions.
  • Developed interactive dashboards and visualizations using Tableau, Power BI, and Matplotlib to present data insights to stakeholders.
  • Applied advanced statistical methods, hypothesis testing, and A/B experimentation to guide product decisions and measure business impact.
  • Engineered feature sets for machine learning models by performing data wrangling, transformation, and dimensionality reduction techniques.
  • Leveraged big data technologies such as Apache Spark and Hadoop to process and analyze large-scale structured and unstructured datasets.
  • Integrated predictive analytics into operational workflows via APIs and automation scripts, enabling data-driven decision-making.
  • Applied deep learning techniques with TensorFlow and PyTorch for image recognition, object detection, and NLP-based classification tasks.
  • Created recommendation systems using collaborative filtering, content-based filtering, and hybrid approaches to enhance personalization.
  • Designed and implemented anomaly detection models for fraud detection, network monitoring, and operational risk management.
  • Conducted exploratory data analysis (EDA) to uncover trends, correlations, and outliers, shaping the direction of data-driven projects.
  • Utilized SQL extensively for complex data querying, joins, aggregations, and optimization to prepare datasets for analysis.
  • Built data pipelines for continuous integration of fresh data from APIs, streaming platforms, and transactional systems.
  • Deployed machine learning models to production using Flask, FastAPI, and Docker, integrating them into live business systems.
  • Used natural language processing to extract structured insights from unstructured datasets such as customer reviews, logs, and social media posts.
  • Implemented NLP techniques for sentiment analysis, topic modeling, and text classification to derive insights from textual data.
  • Developed time-series forecasting models for sales, inventory, and demand prediction using ARIMA, Prophet, and LSTM architectures.
  • Collaborated with cross-functional teams to translate business goals into actionable data science projects with measurable outcomes.
  • Applied clustering algorithms like K-means, DBSCAN, and hierarchical clustering to segment customers and identify behavioral patterns.
  • Conducted model validation, cross-validation, and hyperparameter tuning to ensure accuracy, robustness, and generalizability of results.
  • Documented methodologies, assumptions, and findings to ensure reproducibility and transparency in analytics projects.

Data Science

JM Family Enterprises
08.2018 - 02.2020
  • Developed scalable predictive models for operational optimization, integrating them with business systems to support automated decision-making.
  • Applied advanced time-series analysis techniques, including seasonal decomposition and vector autoregression, to improve demand forecasting accuracy.
  • Designed custom scoring algorithms for risk assessment, credit evaluation, and lead prioritization based on multi-source data inputs.
  • Implemented customer segmentation strategies using clustering algorithms to inform targeted marketing campaigns and personalized offerings.
  • Conducted causal inference analysis to measure the real impact of strategic initiatives and product changes on key metrics.
  • Created interactive, data-driven storytelling visualizations to communicate complex analytical findings to non-technical audiences.
  • Leveraged graph analytics techniques to detect communities, influence networks, and relationships in complex datasets.
  • Designed reinforcement learning models for optimization problems such as pricing strategies and supply chain routing.
  • Performed large-scale A/B testing with statistical rigor, including sample size calculation and power analysis, to ensure valid results.
  • Partnered with engineering teams to optimize data storage, retrieval, and processing pipelines for faster analytical turnaround.
  • Implemented dimensionality reduction techniques like PCA and t-SNE to visualize and interpret high-dimensional datasets.
  • Contributed to the development of organization-wide data standards and best practices for analytics, governance, and reproducibility.
  • Designed predictive maintenance models for industrial equipment using sensor data, enabling proactive service scheduling and reducing downtime.
  • Applied survival analysis techniques to model customer retention patterns and predict churn timelines across subscription-based products.
  • Developed large-scale data integration pipelines that combined transactional, behavioral, and third-party datasets for unified analytics.
  • Created real-time fraud detection systems by integrating streaming analytics platforms with anomaly detection models.
  • Applied ensemble learning strategies to improve predictive performance, combining gradient boosting, bagging, and stacking approaches.
  • Conducted feature selection using statistical methods, permutation importance, and model-based ranking to improve model efficiency.
  • Designed model retraining pipelines triggered by data drift and concept drift detection, ensuring long-term model reliability.
  • Built dashboards with drill-down capabilities for operational teams to explore trends, anomalies, and forecasts interactively.
  • Collaborated with domain experts to incorporate business logic into model development, ensuring relevance and practical utility.

Data Analyst

Edvensoft Solutions India Pvt. Ltd
09.2016 - 04.2018
  • Developed interactive dashboards and visualizations in Tableau, Power BI, and Excel to track KPIs, monitor performance trends, and support strategic planning.
  • Conducted exploratory data analysis (EDA) to identify patterns, anomalies, and correlations, influencing business strategies and process improvements.
  • Created automated reporting solutions using SQL, Python, and Excel macros to reduce manual effort and improve reporting frequency.
  • Performed data validation and quality checks to ensure accuracy, consistency, and completeness of critical business datasets.
  • Designed and implemented SQL queries, joins, and aggregations for extracting insights from relational databases.
  • Collaborated with cross-functional teams to define metrics, build analytical models, and translate findings into actionable recommendations.
  • Analyzed customer behavior and segmentation data to identify target groups for marketing campaigns and product recommendations.
  • Designed and implemented advanced SQL queries and stored procedures to extract, aggregate, and transform large datasets for business analysis.
  • Created business intelligence reports and self-service dashboards that empowered stakeholders to explore data and monitor performance independently.
  • Performed trend, variance, and cohort analyses to identify growth opportunities and operational inefficiencies.
  • Conducted root cause analysis for business problems using statistical techniques, uncovering actionable insights for process improvements.
  • Developed KPI frameworks and performance scorecards to track operational, sales, and marketing metrics.
  • Utilized Python libraries such as Pandas, NumPy, and Matplotlib to automate data analysis workflows and visualization tasks.
  • Built data models and relationships in Power BI to enable dynamic, drill-down reporting across multiple data sources.
  • Partnered with marketing teams to measure campaign effectiveness, track conversion funnels, and optimize targeting strategies.
  • Managed large Excel-based reporting systems, integrating external data connections and automated refresh schedules.
  • Used advanced Excel functions such as pivot tables, VLOOKUP, and Power Query for quick and effective data analysis.
  • Conducted A/B testing and statistical analysis to measure the impact of marketing, product, and operational initiatives.
  • Maintained documentation for data sources, transformation processes, and reporting workflows for long-term usability.
  • Supported data migration and integration projects by validating datasets during system upgrades and platform transitions.

Education

Bachelor of Science - Computer Science

Jawaharlal Nehru Technological University
Kakinad
06-2016

Skills

  • Generative AI expertise
  • Model Development: Fine-tuning, transfer learning, reinforcement learning from human feedback (RLHF)
  • Multimodal AI: Text-to-Image (Stable Diffusion, Midjourney, DALL-E), Text-to-Speech, Speech-to-Text
  • AI Tools & Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, LangChain, OpenAI API, Vertex AI
  • Data Engineering: Feature engineering, vector databases (Pinecone, Weaviate, Chroma)
  • Prompt Engineering: Few-shot, chain-of-thought, retrieval-augmented generation (RAG)
  • Cloud Platforms: AWS SageMaker, Azure AI, Google Cloud Vertex AI
  • DevOps/MLOps: Docker, Kubernetes, CI/CD for AI, MLflow, Airflow
  • Programming: Python, SQL, JavaScript, RESTful APIs
  • Other: Knowledge Graphs, semantic search, API integrations
  • Agile process implementation
  • Experienced with TensorFlow framework
  • Predictive analytics experience
  • Experienced with Scikit-learn library
  • Experience with neural network applications
  • Feature selection expertise
  • Proficient in machine learning
  • System design expertise
  • Advanced analytics proficiency
  • Text analysis expertise
  • Experience with reinforcement learning techniques
  • Algorithm development
  • Statistical modeling
  • Experienced with Keras framework
  • Proficient in deep learning
  • Collaborative version control
  • Linear algebra
  • Calculus optimization
  • Data analytics
  • Software development
  • Pattern recognition
  • Predictive modeling
  • Image processing
  • Speech recognition
  • Big data technologies
  • Information retrieval
  • Teamwork
  • Teamwork and collaboration
  • Problem-solving
  • Time management
  • Attention to detail
  • Problem-solving abilities
  • Multitasking
  • Multitasking Abilities
  • Reliability
  • Excellent communication
  • Critical thinking
  • Critical thinking
  • Organizational skills
  • Team collaboration
  • Active listening
  • Effective communication
  • Adaptability and flexibility
  • Decision-making
  • Collaborative teamwork
  • Team building
  • Self motivation
  • Analytical thinking
  • Interpersonal communication
  • Complex Problem-solving
  • Strong interpersonal skills

Timeline

Generative AI Engineer

Citizens Financial Group
06.2024 - Current

AI/ML Engineer

Chevron Corporation
07.2022 - 05.2024

Data Science

Optum
03.2020 - 06.2022

Data Science

JM Family Enterprises
08.2018 - 02.2020

Data Analyst

Edvensoft Solutions India Pvt. Ltd
09.2016 - 04.2018

Bachelor of Science - Computer Science

Jawaharlal Nehru Technological University

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Charishma Polineni