Open to AI/ML Engineering roles

Hello,
Sai here! 👋

I'm an AI/ML Engineer with 3 years of experience designing and deploying intelligent machine learning and Generative AI solutions across customer analytics and enterprise support automation.

I build LLM-powered applications, RAG pipelines, semantic search systems, and Transformer-based NLP models — then ship them to production.

All things AI: Generative AI. RAG Systems. MLOps.
Yep, I do them all.

Find me
Resume reddysaikumar614@gmail.com
Sai Kumar Reddy
3+
Years building
AI/ML systems
AWS
Solutions Architect
Associate
See the systems running →

Systems
in production

Not prototypes. Retrieval platforms and scoring engines that retrieve across technical documentation and rank accounts by risk — containerized, monitored, and retrained on drift.

Example interface mockup: a support assistant returning a context-aware answer with its retrieved source documents ranked by relevance
Example interface — retrieval-augmented technical support
Example dashboard mockup: anonymised accounts ranked by churn risk score with the signal that triggered each
Example interface — churn risk scoring
Four Teledyne business segments feeding the retrieval layer, with FY2025 net sales share for each
Teledyne segment mix — FY2025 net sales
01

Intelligent Technical Support Automation

Teledyne Technologies Inc · AI/ML Engineer · Jul 2025—Present An enterprise RAG platform that retrieves, ranks and generates context-aware answers from technical manuals, SOPs and years of support history — across four engineering domains that share almost no vocabulary.
RAG architecture: knowledge sources through chunking and vector stores, queries through intent classification, retrieval, reranking, agent orchestration and grounded generation
35%
Fewer support tickets
30%
Better search relevance
18%
Higher response accuracy
40%
Faster deployments
View complete case study
02

Customer Retention Intelligence

Colt Technology Services · Junior Machine Learning Engineer · May 2021—Jun 2023 A churn prediction platform over 250K+ customer records, joining usage, billing and support signals into one view — and turning retention from a reaction into a ranked, workable list.
Churn pipeline: source systems through ETL unification, cleaning, feature engineering, model training, precision recall and F1 evaluation, and FastAPI serving
250K+
Records processed
15%
Accuracy improvement
30%
Faster preprocessing
20%
Lower inference latency
View complete case study
What I build with

The toolkit

From Transformer fine-tuning to cloud-native deployment — the full path from experiment to production.

◆Generative AI & LLMs

LangChainHugging FaceRAGOpenAI APILlamaIndexLangGraphAI Agents

◆ML & Deep Learning

PyTorchTensorFlowScikit-learnKerasTransformersCNNs · RNNs

◆NLP & Vector Search

BERTspaCyPineconeFAISSChromaDBSemantic Search

◆MLOps & Cloud

DockerKubernetesMLflowFastAPIAWS SageMakerCI/CD
Background

Education & certification.

M.S. in Advanced Data Analytics
University of North Texas — Denton, TX
Aug 2023 — May 2025
B.S. in Computer Science
Osmania University — Hyderabad, India
Jun 2018 — May 2021
✦
AWS Certified Solutions Architect — Associate Amazon Web Services

Get in touch

reddysaikumar614@gmail.com
Sai-Kumar-Reddy-Resume.pdf
© Sai Kumar Reddy
AI/ML Engineer · Generative AI · MLOps