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

Machine Learning / AI Engineer with 4+ years of experience owning the full model lifecycle—from problem framing and feature engineering through training, deployment, and production monitoring—for enterprise ML and LLM systems. Skilled in building production-grade APIs, containerized deployments, and CI/CD pipelines. I build scalable data processing and MLOps workflows, including model versioning, drift monitoring, and observability (MLflow, Prometheus, Grafana). I partner with product, data engineering, and software teams in Agile environments, and I document architecture and operational runbooks while communicating trade-offs to technical and non-technical stakeholders.…Machine Learning / AI Engineer with 4+ years of experience owning the full model lifecycle—from problem framing and feature engineering through training, deployment, and production monitoring—for enterprise ML and LLM systems. Skilled in building production-grade APIs, containerized deployments, and CI/CD pipelines. I build scalable data processing and MLOps workflows, including model versioning, drift monitoring, and observability (MLflow, Prometheus, Grafana). I partner with product, data engineering, and software teams in Agile environments, and I document architecture and operational runbooks while communicating trade-offs to technical and non-technical stakeholders.

Karunakara Reddy Julakanti

AI Engineer, Data Scientist, DevOps Developer, +1





Machine Learning / AI Engineer with 4+ years of experience owning the full model lifecycle—from problem framing and feature engineering through training, deployment, and production monitoring—for enterprise ML and LLM systems. Skilled in building production-grade APIs, containerized deployments, and CI/CD pipelines. I build scalable data processing and MLOps workflows, including model versioning, drift monitoring, and observability (MLflow, Prometheus, Grafana). I partner with product, data engineering, and software teams in Agile environments, and I document architecture and operational runbooks while communicating trade-offs to technical and non-technical stakeholders.…Machine Learning / AI Engineer with 4+ years of experience owning the full model lifecycle—from problem framing and feature engineering through training, deployment, and production monitoring—for enterprise ML and LLM systems. Skilled in building production-grade APIs, containerized deployments, and CI/CD pipelines. I build scalable data processing and MLOps workflows, including model versioning, drift monitoring, and observability (MLflow, Prometheus, Grafana). I partner with product, data engineering, and software teams in Agile environments, and I document architecture and operational runbooks while communicating trade-offs to technical and non-technical stakeholders.

Available to hire

Machine Learning / AI Engineer with 4+ years of experience owning the full model lifecycle—from problem framing and feature engineering through training, deployment, and production monitoring—for enterprise ML and LLM systems. Skilled in building production-grade APIs, containerized deployments, and CI/CD pipelines.

I build scalable data processing and MLOps workflows, including model versioning, drift monitoring, and observability (MLflow, Prometheus, Grafana). I partner with product, data engineering, and software teams in Agile environments, and I document architecture and operational runbooks while communicating trade-offs to technical and non-technical stakeholders.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at NVIDIA

January 1, 2025 - Present

Designed asynchronous FastAPI microservices for production AI workloads, integrating vector search, embedding models, and retrieval evaluation frameworks (RAGAS, LangSmith, DeepEval) to improve answer quality and reliability for enterprise assistants serving 500+ concurrent users. Owned the end-to-end RAG platform lifecycle using LangChain and FAISS, indexing 300K+ internal documents and reducing retrieval latency by 20% in production. Built agentic workflows with LangGraph and implemented LoRA (PEFT) for domain-specific LLM applications. Scaled distributed data processing using Apache Spark, PySpark, and Kafka to process 15M+ records daily. Automated MLOps with MLflow, Docker, Kubernetes, and CI/CD, and implemented monitoring/observability for 20+ models using Prometheus, Grafana, and Weights & Biases. Documented architecture and runbooks and collaborated across product, data engineering, and software teams.

Machine Learning Engineer at Infosys

September 1, 2021 - August 1, 2023

Partnered with business stakeholders to frame ML use cases across classification, regression, forecasting, and anomaly detection, defining success metrics and delivering 12+ enterprise AI applications improving operational efficiency by 25%. Engineered data ingestion and ETL pipelines using Python, SQL, PySpark, Pandas, Azure Databricks, Delta Lake, and Unity Catalog, processing 8M+ records and improving data quality. Conducted feature engineering and model evaluation using scikit-learn, TensorFlow, and XGBoost, improving accuracy by 10% via hyperparameter optimization, ensemble learning, and cross-validation. Integrated models into enterprise applications using FastAPI, Docker, MLflow, and REST APIs with model versioning and deployment automation for 10+ production applications. Reduced data preparation time by 20% and accelerated development cycles using Azure Data Factory and Azure Databricks. Collaborated in Agile with data engineers, DevOps, QA, and business analysts; supported CI

AI Engineer / Data Scientist at Cyient

September 1, 2020 - August 1, 2021

Built end-to-end AI and computer vision solutions for image classification, object detection, and defect detection using Python, OpenCV, PyTorch, and TensorFlow to support industrial automation and quality inspection. Developed data preprocessing and deep learning pipelines with augmentation, transfer learning, and CNN architectures to improve accuracy and robustness in real-world conditions. Deployed models as scalable RESTful services using FastAPI and Docker and integrated inference pipelines into enterprise applications with monitoring. Conducted rigorous model training/validation with evaluation metrics such as precision, recall, F1-score, mAP, and confusion matrices and documented experiments for Agile-based delivery.