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Achyutha Pranavi - AI/ML ENGINEER / DATA ANALYST

[email protected]

Location: Jersey City, New Jersey, USA

Relocation: REMOTE

Visa: GC

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Name: ACHYUTHA PRANAVI Role: Senior AI/ML Engineer | Senior Data Analyst Email address: [email protected] Contact: +1 (347) 921-0136 LinkedIn: http://www.linkedin.com/in/achyutha-p-89a3b5216 Professional Summary Results-driven Senior AI/ML Engineer and Senior Data Analyst with 11 years of experience in data science, machine learning, advanced analytics, SAS programming, and AI solutions across finance, healthcare, retail, insurance, and telecom sectors. Expert in cloud-native AI/ML solutions on Microsoft Azure (Azure ML, Databricks, Synapse Analytics, Cognitive Search, Azure OpenAI) and AWS (Redshift, S3, Lambda, Athena, Glue, QuickSight), architecting and deploying scalable, production-ready ML systems. Skilled in Python, TensorFlow, PyTorch, and cloud-based MLOps workflows (AWS SageMaker, Azure ML, Databricks) for developing, optimizing, and deploying ML/AI models, including real-time inference pipelines. Advanced practitioner in Large Language Models (LLMs), Cursor AI, and Generative AI; built retrieval-augmented generation (RAG) pipelines and localization-focused AI solutions using LangChain, Semantic Kernel, FAISS, and Azure Cognitive Search (Azure AI Search) to enhance accuracy and reduce hallucinations. Skilled in optimizing model outputs through fine-tuning, reinforcement learning, and output post-processing for accuracy and relevance. Built multi-agent systems using LangChain, LangGraph, CrewAI, AutoGen, and Cursor AI for intelligent automation in financial research, clinical document processing, and compliance reporting. Developed transformer-based NLP models (BERT, GPT, Azure OpenAI) for document summarization, sentiment analysis, entity detection, and clinical note processing in regulated industries. Applied time-series forecasting models (ARIMA, Prophet, LSTM, Neural Prophet) for financial trend prediction, demand forecasting, and risk assessment. Designed and deployed MLOps and CI/CD pipelines using MLflow, Azure DevOps, Docker, and Kubernetes, ensuring reproducibility, monitoring, governance, and compliance in high-stakes deployments. Engineered feature engineering and ETL pipelines using PySpark, pandas, Azure Data Factory, AWS Glue, and Delta Lake, delivering enriched datasets to improve model performance. Delivered enterprise analytics and business intelligence (BI) solutions using Power BI, Tableau, and QuickSight for real-time KPI tracking and executive reporting. Implemented responsible AI practices with SHAP, LIME, Azure AI Content Safety, and fairness metrics to ensure transparency, explainability, and compliance (HIPAA, financial regulations). Designed predictive models for fraud detection, claims management, and patient stratification, achieving measurable improvements in detection accuracy and operational efficiency. Applied knowledge graph models with Neo4j and NetworkX for mapping complex relationships in fraud detection and compliance analytics. Performed statistical analysis, A/B testing, and cohort analysis to drive targeted marketing, campaign optimization, and revenue growth. Experienced in advanced AI architecture patterns including RAG, Agentic RAG, MCP, Functional Calling, and Agent-to-Agent (A2A) communication within Azure environments, leveraging frameworks like Spring AI, LangChain, LangGraph, LlamaIndex, and Agno. Developed high-volume data pipelines using Snowflake, Alteryx, and distributed computing frameworks for scalable analytics in finance, healthcare, and retail. Leveraged self-supervised learning and contrastive learning for domain-specific pretraining on financial and healthcare text corpora, improving embedding quality for downstream ML tasks. Built synthetic data generation workflows with Gretel.ai and internal tools to address class imbalance while maintaining privacy and compliance. Proven expertise in SQL query optimization and data warehousing (Amazon Redshift, Snowflake, SQL Server, MySQL, Oracle) for near real-time reporting and performance gains. Conducted sales funnel, conversion rate, and clickstream data analysis using Python (pandas, NumPy) and R to identify friction points and growth opportunities. Integrated external datasets (Nielsen, social media sentiment, market data, social determinants of health) with internal data to enrich analytical models. Implemented data governance, lineage tracking, and metadata management using AWS Lake Formation, Unity Catalog, and dbt for compliance and standardization. Automated analytical workflows using Python scripting, AWS Lambda, and Azure Functions, reducing manual workload and enabling real-time insights. Experienced in private equity AI platform development and integration, leveraging cloud-native architectures with Azure infrastructure, Flask-based API services, and secure microservices design. Provided mentorship and cross-functional leadership, delivering knowledge-sharing sessions on MLOps, Generative AI, Azure AI Studio, and translating complex technical outputs into actionable business insights. Technical Skills Programming & Core Technologies Python, Java, JavaScript, SQL, SAS, PySpark, Bash, R, Flask, MSSQL, Linux/Unix Cloud Platforms Microsoft Azure (Azure ML, Azure Databricks, Azure Synapse, Azure Cognitive Search (Azure AI Search), Azure OpenAI), AWS (Redshift, S3, Lambda, Athena, Glue, QuickSight), Snowflake Data Visualization & Business Intelligence Power BI, Tableau, QuickSight, Matplotlib, Seaborn Big Data & ETL PySpark, pandas, Azure Data Factory, AWS Glue, SQL, Alteryx, Delta Lake, Apache Airflow Databases & Data Warehousing Amazon Redshift, Snowflake, SQL Server, MySQL, Oracle, Data Modeling, Query Optimization Statistical Analysis & Business Intelligence Advanced Statistics, A/B Testing, Cohort Analysis, Customer Segmentation, Market Research, Business Intelligence, KPI Development, Forecasting & Demand Planning AI/ML Frameworks & Advanced Learning PyTorch, TensorFlow, scikit-learn, Keras, Cursor AI, CrewAI, AutoGen, XGBoost, Databricks, Hugging Face Transformers, LangChain, LangGraph, Semantic Kernel, Self-Supervised Learning, Contrastive Learning, Pretraining (Financial Text), Spring AI, Agno, Pydantic, Prompt Engineering, Context Engineering, Functional Calling, Azure AI Search, Chunking Strategies for RAG Pipelines NLP & LLMs BERT, GPT, Azure OpenAI, RAG (FAISS, Vector DBs), Localization QA, Multi-lingual NLP, Translation Consistency Checks, Text Summarization, Sentiment Analysis, Entity Recognition Time-Series Forecasting ARIMA, Prophet, LSTM, Neural Prophet Vector & Graph DBs FAISS, Azure Cognitive Search (Azure AI Search), Neo4j, NetworkX, Graph Embeddings, Knowledge Graphs MLOps & DevOps MLflow, Azure DevOps, AWS SageMaker, Docker, Kubernetes, Prometheus, Azure Monitor, Azure ML, Databricks, Real-Time Model Inference Pipelines, CI/CD for ML, Unit Testing, Postman Responsible AI SHAP, LIME, Fairness Metrics, Azure AI Content Safety Synthetic Data & Privacy Gretel.ai, Differential Privacy, Data Augmentation Educational Details Master of Science in Computer Science - University of Central Missouri (Aug 2011 - Dec 2012) Bachelor of Science in Computer Science - Lovely Professional university (Aug 2007 - Jun 2011) Certifications Microsoft Certified: Azure AI Engineer Associate - 2023 AWS Certified Machine Learning - Specialty 2021 Work Experience Client: Jefferies Financial Group Inc, New York, NY May 2024 - Present Role: Senior AI/ML Engineer Responsibilities: Designed and implemented AI/ML solutions on Microsoft Azure using Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics for scalable, secure model training, deployment, and governance. Led development of end-to-end ML pipelines with Python, Azure ML SDK, and MLflow, integrating CI/CD and model governance for reproducibility and operational excellence. Built and deployed advanced NLP/LLM models for financial document summarization, sentiment analysis, risk entity detection, and localization QA, leveraging BERT, Azure OpenAI, and custom transformer architectures. Architected and delivered Azure-hosted agentic AI workflows integrating Spring AI, LangChain, LangGraph, LlamaIndex, and Agno, with Pydantic for schema validation, functional calling, and A2A (Agent-to-Agent) communication. Developed Java-based microservices integrated with AI APIs and hybrid Python-Java solutions for financial analytics pipelines. Integrated vector databases (Azure AI Search, FAISS) and applied embedding & chunking strategies for optimized RAG retrieval, improving context relevance and model output quality. Built multi-agent systems using LangChain, LangGraph, CrewAI, AutoGen, and Cursor AI for intelligent automation in financial research, compliance reporting, and earnings analysis. Created custom RAG pipelines with grounding, context engineering, and prompt engineering to reduce hallucinations and ensure factual accuracy. Enhanced LLM/NLP performance through prompt tuning, PEFT (parameter-efficient fine-tuning), RLHF, and post-processing optimizations. Implemented real-time data integration and inference pipelines for high-frequency financial streams, reducing latency by 35% and enabling sub-second decision-making in trading and manufacturing-style operational systems. Applied time-series forecasting models (ARIMA, Prophet, LSTM) for multi-asset financial trend prediction and risk forecasting. Developed REST APIs with Python Flask, deployed in Azure, as part of a private equity AI core platform; designed unit tests and validated APIs using Postman for production readiness. Participated in Agile/Scrum ceremonies (Story Elaboration, Sprint Planning, Daily Standups, Retrospectives) and contributed to backlog grooming and delivery planning. Applied design patterns (MVC, Singleton, Factory) for scalable, maintainable AI microservices architecture. Implemented application security measures including encryption, SSL/TLS, JWT authentication, and role-based authorization. Built LLM-based workflows for translation and style guide adherence in collaboration with linguists to ensure cultural/contextual accuracy. Leveraged Azure Databricks Delta Lake and Unity Catalog for unified governance, lineage tracking, and secure access to financial data. Engineered feature pipelines using PySpark, pandas, and Azure Data Factory with advanced transformations and temporal joins to improve model performance. Applied knowledge graph modeling (Neo4j, NetworkX) to map customer/transaction/instrument relationships for fraud detection and compliance analytics. Integrated responsible AI tooling (Azure AI Content Safety, SHAP, LIME) for transparency, fairness, and compliance with financial regulations. Created synthetic data generation workflows (Gretel.ai, custom tools) to augment imbalanced datasets and protect sensitive financial information. Developed monitoring dashboards (Power BI, Azure Application Insights, Prometheus) for tracking drift, accuracy, and anomalies in production. Led self-supervised and contrastive learning research for pretraining on proprietary financial text corpora. Applied data mesh principles for scalable ML architectures across global financial domains, enabling decentralized innovation. Mentored engineers and led internal training on MLOps, generative AI, Azure AI Studio, and foundation model adaptation. Built executive reporting suites in Power BI and Azure Synapse Analytics to analyze trading volumes, volatility, and portfolio performance for senior leadership. Client: HCA Healthcare Inc, Nashville, TN Nov 2022 - April 2024 Role: AI/ML Engineer Responsibilities: Designed and deployed scalable machine learning models to automate risk prediction and patient stratification within healthcare plans, improving proactive care delivery. Collaborated with cross-functional healthcare and engineering teams to translate clinical goals into data-driven solutions using Azure Machine Learning Studio and Azure Synapse Analytics. Developed robust data pipelines and orchestrated ETL workflows using Azure Data Factory to ensure accurate ingestion and transformation of large-scale healthcare data. Engineered predictive models for claims fraud detection, leading to a 30% reduction in false claims through advanced supervised learning techniques. Built and maintained CI/CD pipelines for ML model deployment using Azure DevOps, improving delivery speed and ensuring reproducibility across staging and production environments. Utilized PySpark, SQL, and Azure Databricks for distributed data processing and real-time analytics in population health management systems. Applied TensorFlow and PyTorch to develop deep learning models for classification, anomaly detection, and forecasting in healthcare operations. Integrated SageMaker endpoints into production workflows for low-latency inference in patient risk stratification models. Created explainable AI solutions using SHAP and LIME, enabling compliance with healthcare regulations and enhancing model transparency for clinicians. Integrated external social determinant datasets with HCA data on Azure Data Lake to enhance patient outcome modelling through feature engineering and data fusion. Implemented model monitoring dashboards using Power BI and Azure Monitor, allowing continuous tracking of ML performance and drift in real-world settings. Conducted hyperparameter tuning and model optimization leveraging Azure AutoML for accelerated experimentation and model selection. Ensured all AI solutions complied with HIPAA standards, embedding privacy, security, and governance controls into the ML lifecycle. Supported the development of NLP models to analyze unstructured clinical notes, enabling structured data extraction and classification via BERT and spaCy frameworks. Led efforts in data labelling strategies, annotation workflows, and active learning, accelerating model training for medical image and document classification. Participated in design reviews and code audits to enforce MLOps best practices, including containerization using Docker and orchestration with Kubernetes on Azure AKS. Explored early agentic designs using LangChain and Cursor AI for automating clinical document parsing, note classification, and physician Q&A support. Collaborated with stakeholders to define key metrics and success criteria for AI initiatives, aligning ML outputs with the goals of value-based healthcare. Developed anomaly detection and forecasting models to optimize operational workflows in healthcare facilities, applying methodologies transferable to manufacturing production line optimization. Integrated deployed models into scalable cloud pipelines for continuous monitoring and retraining, ensuring sustained performance in dynamic, high-volume environments. Applied transfer learning and deep learning models to diagnostic imaging use cases, improving anomaly detection accuracy across large datasets. Mentored junior engineers and data scientists on Azure ML workflows, code versioning using Git, and experiment tracking with MLflow. Delivered impactful presentations to both technical and non-technical audiences, demonstrating the business value and clinical implications of AI projects. Client: Target Corp, Minneapolis, MN Jan 2019 - Oct 2022 Role: Senior Data Analyst Responsibilities: Led the design and implementation of scalable data pipelines on AWS, utilizing Amazon Redshift, S3, and AWS Glue, to process and store high-volume retail transaction data. Delivered actionable insights by developing complex SQL queries and stored procedures to analyze customer behavior, product trends, and seasonal performance across Target's retail network of 1,800+ locations. Created dashboards using Tableau, Power BI, and Amazon QuickSight for real-time executive reporting on inventory optimization, supply chain efficiency, and retail KPIs, integrating with Athena and Glue Data Catalog for seamless data querying and exploration. Collaborated with cross-functional teams including marketing, merchandising, and e-commerce to align business goals with data strategies, improving campaign targeting by 23%. Executed deep-dive analysis on sales funnels, conversion rates, and clickstream data using Python (pandas, NumPy) to identify friction points in the customer journey. Managed the end-to-end lifecycle of data modeling projects, ensuring consistency across dimensions and metrics using dbt and Snowflake, enhancing data integrity across teams. Designed and optimized ETL workflows using Alteryx to automate ingestion and transformation of financial data. Used SAS to build high-volume retail analytics reports integrating AWS Redshift data, improving reporting speed by 35%. Leveraged Snowflake for scalable data warehousing, supporting RAG pipelines and model-ready datasets for LLMs. Applied advanced statistical methods and regression models to predict demand fluctuations and optimize pricing strategies, contributing to a 12% revenue uplift in 2021. Automated recurring reporting processes with Python scripting and AWS Lambda, reducing manual workload by 40% and improving delivery accuracy. Led data governance initiatives, including data quality checks, lineage documentation, and access control using AWS Lake Formation, ensuring compliance with internal and regulatory standards. Integrated external market datasets (e.g., Nielsen, social media sentiment) to enrich internal datasets and support competitive analysis and localized merchandising efforts. Developed forecasting models using Prophet and ARIMA to assist in inventory planning and reduce overstock scenarios across warehouse hubs. Delivered training sessions and onboarding guides for junior analysts on AWS tools, data interpretation, and visualization best practices. Collaborated with DevOps to integrate data monitoring solutions using CloudWatch, improving system reliability and proactive issue detection. Conducted cohort and churn analysis using SQL and Python, identifying high-value customer segments and helping marketing focus on retention campaigns. Implemented best practices in version control, CI/CD pipelines, and analytics code management using Git, AWS CodeCommit, and Jenkins. Conducted statistical significance testing and confidence interval analysis for A/B tests, ensuring robust decision-making for marketing campaigns and product features across digital and in-store experiences. Built executive-level financial reporting dashboards tracking revenue metrics, profit margins, and ROI across business units, providing real-time insights for C-suite strategic planning and quarterly board presentations. Acted as a key liaison between business stakeholders and technical teams, translating analytical findings into business decisions that directly impacted revenue, customer engagement, and operational efficiency. Client: Allstate, Northbrook, IL Sept 2015 - Dec 2018 Role: Data Analyst Responsibilities: Conducted comprehensive data analysis on policyholder behavior, claim trends, and underwriting data to support strategic insurance pricing decisions and risk assessments. Utilized AWS services (S3, Redshift, Athena, and Glue) to manage and process large insurance data sets securely and efficiently within a cloud-based infrastructure. Developed and maintained interactive dashboards using Tableau and Power BI to track KPIs, claim volumes, and customer retention trends, increasing stakeholder visibility into performance metrics. Designed and implemented ETL pipelines leveraging AWS Glue and Python to automate data ingestion from multiple sources including internal CRM and third-party actuarial datasets. Collaborated with actuarial and underwriting teams to perform predictive modeling using Python (pandas, scikit-learn) for churn prediction and fraud detection in claims processing. Optimized SQL queries on Amazon Redshift to deliver near real-time reporting and improved system performance by 30% in monthly executive reporting. Participated in data governance initiatives to ensure data quality, accuracy, and compliance with insurance regulations including HIPAA and state-level privacy laws. Conducted detailed cohort analyses to identify customer behavior patterns and delivered actionable insights that directly improved cross-sell campaign effectiveness. Led efforts in integrating and standardizing structured and unstructured data across policy, claims, and customer interaction channels, using Python and AWS Lambda functions. Applied advanced statistical methods and machine learning techniques to support pricing optimization and dynamic segmentation of customers. Coordinated with business stakeholders to gather requirements and translate them into analytical models and visual solutions that directly influenced operational decisions. Created data dictionaries and maintained metadata repositories to ensure data transparency and traceability across functional teams and tools. Provided ad hoc reporting and strategic insights to support sales, marketing, and claims departments in aligning performance with business goals. Implemented data anomaly detection scripts in Python and SQL to flag outliers in claim submissions, improving fraud detection by 18%. Collaborated with DevOps and cloud engineering teams to ensure secure and scalable deployment of analytics workflows using AWS IAM, EC2, and CloudWatch. Developed actuarial pricing models using statistical regression techniques and risk assessment algorithms, supporting underwriting decisions and contributing to optimized premium pricing strategies across multiple insurance product lines. Regularly evaluated new market analytics tools and insurance industry trends to enhance analytical capabilities and align with evolving data science practices. Client: Ooma Inc, Sunnyvale, CA Feb 2013 - Aug 2015 Role: Data Analyst Responsibilities: Collaborated with cross-functional teams to analyze large-scale telecom data, improving decision-making processes and increasing operational efficiency by over 20%. Developed and maintained automated dashboards and visual reports using Tableau and Power BI, enabling senior leadership to monitor KPIs in real-time. Executed end-to-end data analysis projects by extracting data from SQL databases, transforming it through ETL pipelines, and performing statistical evaluation. Conducted deep analysis on customer behavior and churn patterns using Python (pandas, NumPy) and R, leading to actionable retention strategies. Worked closely with engineering and marketing teams to align data findings with telecom industry trends, enhancing product targeting and customer segmentation. Implemented data validation and cleansing routines to ensure data integrity and consistency across systems, improving report accuracy by 30%. Led efforts in market segmentation analysis, providing insight into user demographics and usage patterns which supported strategic pricing initiatives. Created predictive models using machine learning techniques to forecast call drop rates and optimize network resource allocation. Processed large datasets using SQL Server and MySQL, optimizing queries for analytical performance. Worked with Oracle databases and basic ETL processes to support telecom analytics. Assisted in migration projects by validating datasets post-transfer, ensuring smooth transition and compliance with industry standards. Supported monthly and quarterly business reviews with trend analysis, highlighting growth opportunities and operational inefficiencies in telecom services. Developed A/B testing frameworks to assess the impact of new telecom features, helping guide product development with data-driven insights. Provided stakeholder training on interpreting data reports and visualizations, promoting a data-driven culture across departments. Analyzed call quality and VoIP metrics, identifying bottlenecks and recommending improvements that enhanced service reliability and customer satisfaction. Maintained documentation for data models, processes, and business logic to ensure transparency and enable future scalability. Keywords: cprogramm continuous integration continuous deployment quality analyst artificial intelligence machine learning business intelligence sthree active directory rlang information technology trade national California Illinois Minnesota New York Tennessee