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AI/ML Engineer with 5+ years of experience designing and deploying scalable Machine Learning, Generative AI, and cloud-native solutions across enterprise environments. Proven expertise in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, and distributed ML pipelines using Python, FastAPI, AWS, Kubernetes, and Databricks. Passionate about transforming complex business challenges into intelligent, production-ready AI systems that deliver measurable impact through scalable architecture, automation, and data-driven innovation.…AI/ML Engineer with 5+ years of experience designing and deploying scalable Machine Learning, Generative AI, and cloud-native solutions across enterprise environments. Proven expertise in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, and distributed ML pipelines using Python, FastAPI, AWS, Kubernetes, and Databricks. Passionate about transforming complex business challenges into intelligent, production-ready AI systems that deliver measurable impact through scalable architecture, automation, and data-driven innovation.

Govardhan Reddy Narala

AI Engineer, Data Scientist, Software Engineer, +2

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AI/ML Engineer with 5+ years of experience designing and deploying scalable Machine Learning, Generative AI, and cloud-native solutions across enterprise environments. Proven expertise in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, and distributed ML pipelines using Python, FastAPI, AWS, Kubernetes, and Databricks. Passionate about transforming complex business challenges into intelligent, production-ready AI systems that deliver measurable impact through scalable architecture, automation, and data-driven innovation.…AI/ML Engineer with 5+ years of experience designing and deploying scalable Machine Learning, Generative AI, and cloud-native solutions across enterprise environments. Proven expertise in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, and distributed ML pipelines using Python, FastAPI, AWS, Kubernetes, and Databricks. Passionate about transforming complex business challenges into intelligent, production-ready AI systems that deliver measurable impact through scalable architecture, automation, and data-driven innovation.

Available to hire

AI/ML Engineer with 5+ years of experience designing and deploying scalable Machine Learning, Generative AI, and cloud-native solutions across enterprise environments. Proven expertise in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, and distributed ML pipelines using Python, FastAPI, AWS, Kubernetes, and Databricks.

Passionate about transforming complex business challenges into intelligent, production-ready AI systems that deliver measurable impact through scalable architecture, automation, and data-driven innovation.

Skills

Experience Level

Expert

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Beginner

Beginner

Work Experience

AI/ML Engineer at Anthropic

April 1, 2025 - Present

Developed Python-based AI orchestration services using MCP, FastAPI, and asynchronous workflows to connect enterprise tools, enabling secure contextual interactions for over 18,000+ internal users. Engineered Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases, improving response accuracy by 32% with more relevant, context-aware enterprise retrieval. Optimized tool-calling and function orchestration frameworks to reduce inference costs by 28% via intelligent routing, caching, and efficient context management. Designed and implemented MCP-compatible APIs using FastAPI and Pydantic to integrate with GitHub, Slack, databases, and enterprise systems. Built scalable agent workflows (tool discovery, function execution, multi-step reasoning) reducing average response latency by 41%. Architected cloud-native microservices on AWS using Docker, Kubernetes, and Amazon EKS with secure OAuth/JWT/RBAC and implemented deployment/observability for reliable production r

Machine Learning Engineer at Accenture

January 1, 2020 - November 30, 2023

Built Python and PySpark pipelines on Databricks to process high-volume IoT sensor streams for predictive analytics, feature engineering, and scalable ML workflows in manufacturing. Trained TensorFlow and Scikit-learn models for equipment failure prediction, improving accuracy by 23% and enabling proactive maintenance recommendations. Developed computer vision defect detection using Python, OpenCV, and TensorFlow, improving inspection accuracy by 18%. Created feature engineering and ML lifecycle frameworks using Databricks, Spark SQL, Delta Lake, and MLflow (experimentation, versioning, monitoring, reproducibility). Implemented real-time ingestion using Apache Kafka and Python for data from 12,000+ devices. Conducted distributed training/optimization with Databricks, MLflow, TensorFlow, and PyTorch, reducing inference cost by 27%. Architected cloud-native microservices with Docker/Kubernetes/FastAPI on AWS, built event-driven integrations, designed scalable data lake architectures with