AI/ML Engineer with Enterprise Automation Focus

ROHITH CHALLA

+1-862-***-**** **********@*****.*** www.linkedin.com/in/challarohith https://github.com/RohithChalla04 Education

Pace University, New York, USA (August 2023 – December 2024) Master of Science - Data Science- GPA: 3.52

SRM Institute of Science and Technology, Chennai, India (August 2019 – May 2023) Bachelor of Technology - Electronic and Computer Engineering - GPA: 8.40 Experience

AI/ML Engineer Perfect Solution groups (September 2025 – Present)

Currently designing, developing, and fine-tuning machine learning and deep learning models, including data preprocessing, algorithm implementation, and real-world integration.

Writing efficient, well-documented code (Python, TensorFlow, PyTorch, Scikit-learn), testing and optimizing models for accuracy and scalability, and collaborating with teams while staying updated on AI advancements.

Developed end-to-end ML pipelines for classification and prediction tasks, automating data ingestion, training, and evaluation workflows to reduce manual setup time by 40%.

Integrated Large Language Models (LLMs) and Generative AI APIs into enterprise products, enabling context-aware text generation and NLP-based automation.

Implemented model explainability and interpretability tools (SHAP, LIME) to help business teams understand key decision factors and improve trust in AI systems.

Deployed trained models using Docker and AWS Sagemaker, ensuring reproducibility, scalability, and low-latency inference for production workloads.

Collaborated with data engineers and MLOps teams to create continuous training and monitoring frameworks, improving accuracy drift detection and retraining efficiency by 25%.

Enterprise Automation Associate — Maturecloud (Aug 2025 – Sep 2025)

Supported the design and development of AI agents and enterprise integrations leveraging AWS, Workday, and Workato cloud platforms.

Built and configured Workday EIB (Enterprise Interface Builder) integrations to streamline business process workflows and ensure accurate data exchange.

Developed and deployed Workato automations for enterprise workflows, contributing to scalable, low-code integration solutions across HR and finance processes.

Automated data validation and error handling using Workato recipes integrated with AWS Lambda, improving reliability and reducing manual intervention by 40%.

Collaborated with cross-functional teams to design AI-driven chatbot assistants for internal HR queries, improving employee support response times by 60%.

Optimized API-based integrations between Workday and Salesforce using Python and REST APIs, resulting in a 25% reduction in processing latency.

AI Engineer Intern — Cortracker 360 (Feb 2025 – Aug 2025)

Designed and fine-tuned machine learning and deep learning models to solve real-world business problems across multiple domains.

Fine-tuned Large Language Models (LLMs) like GPT and BERT for domain-specific tasks, improving performance on NLP applications.

Developed and deployed LLM-based automation pipelines, integrating Shell scripting for data preprocessing, model orchestration, and deployment automation to enhance scalability and maintainability.

Built custom training pipelines using PyTorch and Hugging Face Transformers, accelerating fine-tuning processes by 30%.

Developed data augmentation and preprocessing scripts in Shell and Python to standardize multi-source datasets for model ingestion.

Collaborated with the MLOps team to integrate continuous model evaluation and monitoring into CI/CD pipelines using Docker and GitHub Actions.

Intern — PROFISOLUTIONS PVT LTD (Oct 2022 – Apr 2023)

Worked on Programmable Logic Controllers (PLC), developing automation logic and control flow for industrial process optimization.

Implemented AES Encryption techniques to enhance data security in device communication systems.

Collaborated on integrating PLC-based systems with encrypted data channels, ensuring reliable and secure data transmission between modules.

Certifications

AWS Machine Learning Engineer Associate (MLA-C01)

AWS Cloud Practitioner Certificate (CLF-C02)

AWS AI Practitioner (AIF-C01)

Azure AZ-900

Databricks Fundamentals Accreditation

SQL Certificate – HackerRank

Deep Neural Networks with PyTorch - (IBM)

Workday EIB Integrations

Workato Pro Automation I, II, III

Projects

AI-Powered Expense Receipt Classifier using Workato and Workday

Developed a Workato automation that integrated with Workday EIB and RAAS to extract employee expense receipts and process them through OpenAI-powered classification models, automatically distinguishing itemized vs. non-itemized reports.

Optimized financial workflows by embedding AI-driven validations back into Workday, reducing manual review effort by 70% and improving compliance accuracy across enterprise expense reporting. Code Assistant – Local LLM-Powered Developer Helper

Collaboratively built an interactive AI code assistant using a locally hosted LLM (via Ollama) and Gradio, simulating a lightweight pair programmer to deliver real-time code explanations and debugging help.

Implemented backend APIs with a focus on team extensibility, integrating prompt history and streaming-free inference, reducing latency by 30% and enhancing overall developer productivity. Speech-to-Speech Emotion Transfer using Whisper and SpeechBrain

Developed a real-time speech-to-speech emotion transfer system using Whisper and SpeechBrain, achieving 85% emotion accuracy and 90% intelligibility based on 50+ user tests.

Integrated Whisper (ASR), SpeechBrain (SER + TTS), and Gradio into a lightweight pipeline that runs entirely on CPU, achieving end- to-end inference in under 5 seconds per sample without requiring GPU or external APIs. Predictive Maintenance Pipeline for Machinery

Led a cross-functional initiative to build a predictive maintenance system using the NASA turbofan dataset, developing ML models to estimate RUL and reduce unplanned downtime.

Integrated solutions with AWS S3 and Apache Airflow, improving pipeline scalability and coordination, resulting in a 50% reduction in manual monitoring.

Technical Skills

Programming languages: Python, SQL, Shell scripting

Deep Learning: CNNs, RNNs, LSTMs, GRUs, Transformers, GANs.

Machine Learning Frameworks: TensorFlow, PyTorch, and Scikit-learn.

NLP and Generative AI Models: BERT, Word2Vec,

CodeLlama, Google Gemma 2.

Techniques and Applications: Tokenization, Named Entity Recognition (NER), Word Embeddings, Text Summarization, Conversational AI, Retrieval-Augmented Generation (RAG), Sequence-to-Sequence Models, Attention Mechanisms, LLM Fine-tuning, Multi-Agent Systems, and Stateful AI

Workflows.

Tools and Frameworks: Langchain, Hugging Face

Transformers, OpenAI API, Ollama, SQL Toolkit, Agent Type, Crew AI, Lamini, and LangGraph, Whisper, SpeechBrain, Postman, Workato, Workday EIB.

Search and Databases: Hybrid Search with Vector Databases

(FAISS, AstraDB), GraphDB, Cypher Query Language,

Knowledge Graph-based Q&A.

UI/UX Frameworks: Streamlit and Gradio

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