Chenchaiah Mekalathuru (Chenchu)

AI Researcher

Austin, Texas, United States6 yrs 1 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in developing scalable AI-driven solutions.
Proven track record in enhancing AI capabilities for education.
Strong leadership in cross-functional AI/ML projects.

Stackforce AI infers this person is a SaaS-focused AI/ML Engineer with a strong emphasis on educational technology.

Contact

mekalathuru.chenchaiah@gmail.com LinkedIn

Skills

Core Skills

Machine LearningNatural Language ProcessingComputer Vision

Other Skills

AI AgentsAI CapabilitiesAgile MethodologiesAlgorithm AnalysisAlgorithmsAnacondaAndroidArtificial Intelligence (AI)BERT TransformersBusiness Process ImprovementC++CUDACalculusCloud ComputingComputer Architecture

About

Experienced AI/ML Engineer with a deep AI research background and a strong track record of developing and managing scalable, AI-driven solutions. Currently advancing Liquid Neural Networks and Generative AI research at the Applied Artificial Intelligence lab, University of South Dakota, with a focus on NLP, Transformers, and vector-based AI applications. With expertise in cloud platforms like AWS and Azure, I bring hands-on experience in integrating AI/ML pipelines and automating DevOps processes across cloud-native infrastructures. Previously, as a Senior ML Engineer at BYJU'S, I led cross-functional teams to exceed performance benchmarks, managing complex projects from ideation through strategic solutions . Proficient in tools and technologies like LangChain, TensorFlow, PyTorch, Docker, Kubernetes, and infrastructure automation (Terraform, CloudFormation), I am adept at building CI/CD pipelines for AI/ML workloads and deploying containerized applications in environments like AWS ECS and EKS. A quick learner and T-shaped professional, I am passionate about leveraging AIOps and vector databases like Pinecone and FAISS for predictive analytics and operational automation. I thrive in collaborative settings, bringing a blend of technical depth and strategic management skills to every project. Let’s connect to discuss how I can contribute to driving AI-driven innovations and operational efficiencies within your team.

Experience

6 yrs 1 mo

Total Experience

1 yr 10 mos

Average Tenure

11 mos

Current Experience

Infosys

AI/ML Engineer

Jul 2025 – Present · 11 mos · Seattle, Washington, United States · On-site

Applied artificial intelligence (𝟚ᗩ𝕀) club

President

Aug 2024 – Feb 2025 · 6 mos · Vermillion, South Dakota, United States · On-site

Lead peer learning sessions on AI basics and guided the development of a mini chat GPT model from scratch.
Engage in research discussions on cutting-edge AI topics to stay updated with industry trends.
Facilitate career path building by providing insights from industry experts at MAANG companies.

Usd applied artificial intelligence research lab (𝟚ᗩ𝕀)

Research Assistant

Aug 2023 – Feb 2025 · 1 yr 6 mos · Vermillion, South Dakota, United States · On-site

Byju's

Senior Machine Learning Engineer

Nov 2019 – Jul 2023 · 3 yrs 8 mos · Hyderabad, Telangana, India · On-site

Boosted revenue generation by accomplishing $600k/month in sales leads as measured by achieving an SSR of 80% with 2 million monthly hits by building BYJU's Q&A search system using neural search techniques.
Enhanced search accuracy and user experience by accomplishing an F1 score of 0.96, Text SSR at 91%, and Image SSR at 60% as measured by production outcomes by developing a universal search feature leveraging BERT Transformers for query intent recognition and entity extraction.
Optimised AI capabilities for educational applications by accomplishing a 13% more accuracy on GSM8k datasets as measured by fine-tuning open-source LLMs like LLaMA and Falcon using QLoRA and PEFT frameworks for math reasoning and chatbot solutions.
Scalability and Agile Development: Managed product feature launches that resulted in a 40% revenue increase within three months, reflecting your skills in scaling solutions critical in AI system deployment and scaling AI/ML pipelines.
Enhanced classroom engagement tracking by accomplishing a mAP50-95 score of 0.922 as measured by real-time monitoring accuracy in classrooms by fine-tuning YOLOv5 models on annotated data to detect upper body objects effectively.

Neural Search TechniquesBERT TransformersFine-tuning LLMsYOLOv5 ModelsMachine LearningNatural Language Processing