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AI/ML Engineer with 5+ years of experience designing and deploying production-scale machine learning systems, Generative AI applications, and autonomous AI agents. Specialized in building LLM-powered multi-agent workflows, RAG systems, and cloud-native ML platforms. Proven track record of improving model accuracy, reducing inference latency, and delivering enterprise-grade AI solutions through distributed systems, MLOps, and modern software engineering practices using Python, PyTorch, LangGraph, FastAPI, and AWS.…AI/ML Engineer with 5+ years of experience designing and deploying production-scale machine learning systems, Generative AI applications, and autonomous AI agents. Specialized in building LLM-powered multi-agent workflows, RAG systems, and cloud-native ML platforms. Proven track record of improving model accuracy, reducing inference latency, and delivering enterprise-grade AI solutions through distributed systems, MLOps, and modern software engineering practices using Python, PyTorch, LangGraph, FastAPI, and AWS.

Akash Sampath

AI Engineer, Data Scientist, Software Engineer

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AI/ML Engineer with 5+ years of experience designing and deploying production-scale machine learning systems, Generative AI applications, and autonomous AI agents. Specialized in building LLM-powered multi-agent workflows, RAG systems, and cloud-native ML platforms. Proven track record of improving model accuracy, reducing inference latency, and delivering enterprise-grade AI solutions through distributed systems, MLOps, and modern software engineering practices using Python, PyTorch, LangGraph, FastAPI, and AWS.…AI/ML Engineer with 5+ years of experience designing and deploying production-scale machine learning systems, Generative AI applications, and autonomous AI agents. Specialized in building LLM-powered multi-agent workflows, RAG systems, and cloud-native ML platforms. Proven track record of improving model accuracy, reducing inference latency, and delivering enterprise-grade AI solutions through distributed systems, MLOps, and modern software engineering practices using Python, PyTorch, LangGraph, FastAPI, and AWS.

Available to hire

AI/ML Engineer with 5+ years of experience designing and deploying production-scale machine learning systems, Generative AI applications, and autonomous AI agents. Specialized in building LLM-powered multi-agent workflows, RAG systems, and cloud-native ML platforms.

Proven track record of improving model accuracy, reducing inference latency, and delivering enterprise-grade AI solutions through distributed systems, MLOps, and modern software engineering practices using Python, PyTorch, LangGraph, FastAPI, and AWS.

Skills

Experience Level

Expert

Expert

Expert

Expert

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Expert

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Expert

Expert

Language

Work Experience

AI/ML Engineer at Anthropic

April 1, 2024 - Present

Developed autonomous software engineering agents using Python, PyTorch, LangGraph, and MCP for repository analysis, multi-step planning, code generation, and debugging workflows for 180,000+ monthly users. Improved task completion accuracy by 34% using advanced prompt orchestration, retrieval-augmented reasoning, agent memory optimization, and structured evaluation pipelines. Reduced end-to-end inference latency by 41% using semantic context retrieval, response caching, asynchronous tool execution, parallel agent orchestration, and optimized long-context processing. Built scalable retrieval pipelines using FAISS, PostgreSQL, Redis, Voyage embeddings, and Pydantic for semantic code search and dependency-aware context retrieval. Designed evaluation frameworks (HumanEval, SWE-bench, MLflow, OpenTelemetry) to benchmark reasoning quality and tool usage, enabling continuous performance improvements. Architected cloud-native microservices with FastAPI, Docker, Kubernetes, Kafka, Redis, and AW

Machine Learning Engineer at Accenture

May 1, 2020 - July 31, 2023

Built real-time customer intent prediction pipelines using Python, PyTorch, and Hugging Face Transformers for multi-channel intent classification and personalization. Improved recommendation accuracy by 32% with hybrid collaborative filtering and deep learning models for next-best-action predictions. Reduced inference latency by 41% for high-volume scoring APIs using FastAPI, Redis caching, and asynchronous processing. Designed and deployed NLP pipelines (BERT-based models, spaCy) for intent detection and sentiment analysis to support automated customer support. Developed churn prediction, conversion scoring, and upsell recommendation models using scikit-learn and XGBoost. Engineered large-scale streaming/batch data pipelines with Spark, Kafka, and Airflow, and deployed ML workloads on AWS (S3/EC2/SageMaker). Improved throughput by 35% and reduced infrastructure cost by 28% via distributed processing and autoscaling. Integrated event-driven decisioning with Kinesis, DynamoDB, and Lambd