AI/ML Engineer & Data Scientist

Parvathi Malle

AI ENGINEER Data Scientist ML Engineer

Oklahoma City, OK 737-***-**** ***************@*****.*** LinkedIn SUMMARY

• AI/ML Engineer with 6+ years of experience across Machine Learning, Generative AI, Predictive Analytics, Data Engineering, and Business Intelligence, delivering enterprise AI solutions across CRM, client operations, product intelligence, supply chain, customer support, and analytics platforms.

• Experienced in building production-ready AI/LLM applications, including Generative AI workflows, conversational AI systems, RAG-based assistants, LLM rankers, recommendation systems, and human-in-the-loop feedback models to improve automation, personalization, knowledge search, and enterprise decision-making.

• Strong hands-on experience with Machine Learning and Data Science models for forecasting, predictive maintenance, product health scoring, failure-risk prediction, personalization, and operational analytics using Python, SQL, PySpark, XGBoost, LightGBM, Prophet, LSTM models, Elasticsearch, and behavioral data.

• Skilled in scalable MLOps, LLMOps, and data pipeline development, including feature engineering, model deployment, monitoring, post-launch optimization, CI/CD, regression validation, SQL analysis, cross-environment debugging, and production support for high-impact enterprise systems.

• Strong stakeholder management and leadership experience, partnering with senior leadership, product, engineering, QA, data science, business, and cloud teams to define AI use cases, align technical roadmaps with business goals, manage cross-functional delivery, and deliver measurable outcomes including 80%+ reduction in mismatch selections, 25% improvement in data reliability, and 40% faster model deployment.

TECHNICAL SKILLS

AI Tools & frame works:

Programming Language:

LLMs, Lang chain, Nvidia NIM, NEMO, RHOAI, AI Agents & Agentic AI. Python, SQL, R, PowerShell, Golang (for backend automation and cloud services) Databases: MySQL, PostgreSQL, NoSQL, Oracle, Essbase, Snowflake (Data Warehousing & Analytics)

NumPy, Pandas, Matplotlib, H2O Driverless AI

AWS (EC2, S3, Lambda, RDS, RedShift, EKS, EMR, Cloud Watch), Snowflake, Azure Power BI, Tableau, MS Excel, SmartView.

Agile, Waterfall.

Git, GitHub.

Data Cleaning, Data Wrangling, Data Warehousing, Data Governance, Database Systems. Data Mining, A/B Testing, Statistical Modeling, Critical Thinking, Problem Solving. Communication Skills: Communication Skills, Presentation Skills, Stakeholder Management, Cross-functional Collaboration. Automation & CI/CD: Terraform, Ansible, Jenkins, Helm, ArgoCD, GitOps. Monitoring & Troubleshooting: Azure Monitor, Datadog, Elasticsearch, Logstash, Kafka, AWS CloudWatch Data Pipelines & ETL:

Security & Compliance:

AWS Lambda, Apache Airflow, Snowflake, RedShift, RDS. IAM, Security Controls, Cloud Security, Data Encryption, GDPR Compliance, SOC Compliance

EDUCATION

Oklahoma City University OK, USA

Master of Science in Computer Science May 2024

Libraries:

Cloud Technologies:

Data Visualization Tools:

Methodologies:

Version Control Tools:

Analytical Skills:

Data Mining:

PROFESSIONAL EXPERIENCE

Paycom, Ok

Senior Machine Learning Engineer Dec 2023 – Current

• Served as a Senior Machine Learning Engineer at Paycom, building enterprise AI solutions for internal CRM, client operations, personnel management, knowledge automation, and workflow intelligence across platforms supporting 7M+ individuals.

• Architected and deployed core capabilities of IWant, Paycom’s command-driven Generative AI engine built on a single database, using LLMs to interpret user intent, extract entities, and deliver context-aware HR and enterprise information retrieval at scale.

• Built high-performance REST and GraphQL APIs using Java Spring Boot to power real-time HR data retrieval and custom LLM workflows, supporting 100K+ queries per day and handling hundreds of requests per second with low-latency performance.

• Developed and deployed end-to-end AI/LLM applications, including Generative AI workflows, conversational AI systems, custom- trained models, LLM rankers, human-in-the-loop feedback loops, and reinforcement learning designs to improve automation, personalization, documentation, and enterprise knowledge management.

• Improved client experience through AI-powered personalization and recommendation systems using LLM ranking models, Elasticsearch, predictive modeling, and behavioral data, reducing mismatch selections by 80%+ and improving the accuracy of product and workflow recommendations.

• Contributed to multi-agent orchestration systems with MCP, RAG, custom agents, OpenCode, GitLab MCP, and Playwright MCP to support internal enterprise workflows for security vulnerability analysis, code triage, code reviews, testing automation, and architecture support.

• Shipped 3 production AI features independently using agentic AI workflows and custom agents, compressing typical multi-person, two-month delivery cycles into approximately 2 weeks per feature while maintaining production quality and business alignment.

• Strengthened production AI and analytics systems by building scalable data pipelines, feature engineering workflows, model deployment processes, post-launch monitoring, and optimization frameworks, improving CRM data reliability and debugging efficiency by 25% through SQL analysis, log-driven investigation, and cross-environment validation.

• Led cross-functional AI delivery across teams of 2–20 contributors, coordinating with business, data, engineering, QA, and platform teams to move AI projects from use-case discovery to design, development, production deployment, testing, adoption, and measurable business impact.

• Mentored junior engineers through code reviews and technical design sessions, authored architecture documentation, led incident response for high-severity production issues, and supported responsible AI adoption through internal AI governance standards aligned with ISO 42001.

Dell Technologies, Ok

Data Scientist Aug 2022 – Dec 2023

• Served as a Senior Data Scientist within Dell Technologies’ Enterprise AI, Product Intelligence, and Advanced Analytics team, building AI/ML solutions across infrastructure products, client devices, supply chain operations, customer support, and services analytics, improving product reliability by 20% and supporting faster data-driven decision-making across business teams.

• Built forecasting and demand prediction models using Python, SQL, PySpark, XGBoost, LightGBM, Prophet, LSTM models, sales data, customer usage patterns, supply chain signals, and product demand trends, improving forecast accuracy by 25% and helping planning teams optimize inventory, production capacity, and go-to-market readiness.

• Developed predictive maintenance, product health, and failure-risk models using device telemetry, service logs, warranty claims, configuration data, support tickets, and manufacturing quality records to identify high-risk systems, reduce repeat failures, improve service planning, and lower support turnaround time by 30%.

• Designed Generative AI assistants and AI agent workflows using RAG, Azure OpenAI, GPT models, Llama, Mistral, Hugging Face, LangChain, LlamaIndex, vector databases, embeddings, and prompt engineering to help engineering, support, and sales teams quickly search product manuals, troubleshooting guides, knowledge articles, warranty policies, service records, and technical documentation, reducing document search time by 60%.

• Led stakeholder management and production delivery by partnering with product engineering, customer support, services, supply chain, sales operations, IT, and cloud engineering teams to define AI use cases, validate model outputs, explain predictions, and maintain scalable MLOps/LLMOps pipelines using Databricks, Azure ML, AWS SageMaker, MLflow, Airflow, Docker, Kubernetes, CI/CD, drift monitoring, alerting, and automated retraining, reducing model deployment time by 40%. Accenture, India

Associate Data Analyst Feb 2020 - Jul 2022

• Extracted, transformed, and validated raw data from MySQL databases, applying data quality checks, error handling, and reconciliation steps to ensure accurate and reliable reporting.

• Developed complex SQL queries using joins, subqueries, CTEs, and window functions to analyze customer purchase behavior, sales trends, and financial performance, generating actionable insights for business teams.

• Cleaned and standardized large datasets using Python and Pandas, handling missing values, duplicates, and inconsistent formats to improve data quality and prepare datasets for reporting and analysis.

• Designed and delivered interactive Power BI dashboards with key metrics, KPIs, drill-through reports, slicers, and custom visuals, providing stakeholders with real-time visibility into customer behavior, sales performance, and operational trends.

• Integrated multiple data sources into Power BI, ensuring automated refreshes, report accuracy, and seamless availability of updated insights for ongoing business analysis.

• Partnered with cross-functional teams to present insights, explain trends, and recommend data-driven strategies that supported marketing decisions, improved reporting efficiency, and strengthened stakeholder decision-making.

• Applied strong analytical and problem-solving skills to identify process gaps, recommend improvements, and support financial process optimization, contributing to a 15% improvement in business efficiency.

• Managed 5+ concurrent analytics projects with tight deadlines, maintaining report accuracy, stakeholder communication, and successful delivery with minimal supervision.

• Utilized database systems to manage and report on financial data, ensuring accuracy of reports supporting nearly $10M in monthly revenue.

PROJECTS

AI-Powered Predictive Maintenance & Quality Intelligence Platform

• Engineered an end-to-end Machine Learning platform for predictive maintenance and manufacturing quality analytics using Python, FastAPI, PySpark, MLflow, and Databricks, processing equipment telemetry, sensor data, inspection logs, and maintenance records to predict failures, detect anomalies, and reduce unplanned downtime by 30%.

• Developed and fine-tuned multiple AI/ML models, including XGBoost, LightGBM, LSTM, Transformer-based time-series models, Isolation Forest, Autoencoders, YOLOv8, Vision Transformers (ViT), and CLIP, to support failure prediction, anomaly detection, defect classification, root-cause analysis, and automated visual inspection across production environments.

• Built a production-ready MLOps architecture using Docker, Kubernetes, MLflow Model Registry, Airflow, PostgreSQL, Redis, and CI/CD pipelines, enabling automated model training, versioning, deployment, monitoring, drift detection, and retraining for scalable enterprise AI/ML services

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