Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Software and Machine Learning Engineer with 3+ years of experience specializing in Generative AI, NLP, and Computer Vision, building LLM-powered RAG pipelines, multimodal systems, and AI safety frameworks. Known for architecting end-to-end ML solutions and improving accuracy, reliability, and operational efficiency in production environments.…

Akhil Reddy

Data Scientist, Full Stack Developer, Web Developer, +5





Software and Machine Learning Engineer with 3+ years of experience specializing in Generative AI, NLP, and Computer Vision, building LLM-powered RAG pipelines, multimodal systems, and AI safety frameworks. Known for architecting end-to-end ML solutions and improving accuracy, reliability, and operational efficiency in production environments.…

Available to hire

Software and Machine Learning Engineer with 3+ years of experience specializing in Generative AI, NLP, and Computer Vision, building LLM-powered RAG pipelines, multimodal systems, and AI safety frameworks. Known for architecting end-to-end ML solutions and improving accuracy, reliability, and operational efficiency in production environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Advanced

Telugu

Advanced

Work Experience

Software Engineer, AI/ML at Science Soft

March 1, 2025 - Present

Design and train machine learning models using Python, improving prediction accuracy by 15-20% across large-scale enterprise datasets. Architected a low-latency inference pipeline using PyTorch, delivering P95 latency under 500ms in a distributed production environment to support high-throughput financial workloads. Streamlined MLOps workflows by implementing CI/CD pipelines, accelerating ML model deployment cycles from 2 weeks to 3 days. Deployed and managed scalable ML microservices on GCP Vertex AI using Docker & Kubernetes, integrating MLflow for experiment tracking and drift monitoring to ensure adherence to financial compliance and regulatory standards. Implemented a Retrieval-Augmented Generation (RAG) pipeline with FAISS vector search, exposed via REST APIs, reducing false negatives on policy data by 18% and improving document-level decision accuracy. Enhanced compliance operations by reducing false escalations by 29% and boosting analyst throughput by 2.3× through a Human-in-

Applied Machine Learning at Bytecraft System

April 1, 2021 - July 1, 2023

Led end-to-end design and development of personalization models for an e-commerce pricing engine using Logistic Regression, XGBoost, and neural networks, driving a 20% increase in customer engagement and a 15% uplift in new user sign-ups. Built distributed data pipelines with Apache Spark, Airflow, and Databricks on AWS EC2 to process 100M+ customer records for downstream training and inference. Integrated computer vision CNN embeddings via TensorFlow to improve recommendation accuracy by 12% and deliver more contextually relevant user experiences. Deployed and monitored production models on AWS SageMaker with real-time REST APIs supporting 2M+ monthly active users at under 200ms latency. Designed and implemented a scalable A/B testing framework to reduce model iteration cycles by 30%. Reduced cloud compute costs by 25% by automating and optimizing ML workflows via Git-based CI/CD.

Applied Machine Learning Engineer at Bytecraft System

April 1, 2021 - July 1, 2023

Led the end-to-end design and development of personalization models for an e-commerce pricing engine using Logistic Regression, XGBoost, and Neural Networks, driving a 20% increase in customer engagement and a 15% uplift in new user sign-ups. Built and maintained distributed data pipelines using Apache Spark, Airflow, and Databricks on AWS EC2, processing and transforming 100M+ customer records to power downstream ML model training and inference workflows. Integrated Computer Vision CNN embeddings via TensorFlow into the product recommendation engine, improving recommendation accuracy by 12% and delivering more contextually relevant user experiences at scale. Deployed and monitored production ML models on AWS SageMaker, engineering real-time REST APIs capable of serving 2M+ monthly active users with a consistent latency of under 200ms. Designed and implemented a scalable A/B testing framework to evaluate model performance across product variants, reducing model iteration cycles by 30%