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I am an AI/ML engineer based in New York with 4+ years of experience building scalable machine learning, deep learning, generative AI, and multi-agent AI systems. I specialize in production-grade agentic AI applications, RAG pipelines, vector databases, memory-enabled agents, and cloud-native AI architectures. I enjoy turning research into robust, observable AI solutions that automate complex workflows and improve automation and decision-making at enterprise scale. Throughout my career at Scale AI and IBM, I have designed and deployed enterprise-grade AI platforms, tuned LLMs, and led end-to-end ML/Ops pipelines. I thrive in collaborative, cross-functional teams and am passionate about building systems that enhance automation, reasoning, and operational efficiency for large organizations.…I am an AI/ML engineer based in New York with 4+ years of experience building scalable machine learning, deep learning, generative AI, and multi-agent AI systems. I specialize in production-grade agentic AI applications, RAG pipelines, vector databases, memory-enabled agents, and cloud-native AI architectures. I enjoy turning research into robust, observable AI solutions that automate complex workflows and improve automation and decision-making at enterprise scale. Throughout my career at Scale AI and IBM, I have designed and deployed enterprise-grade AI platforms, tuned LLMs, and led end-to-end ML/Ops pipelines. I thrive in collaborative, cross-functional teams and am passionate about building systems that enhance automation, reasoning, and operational efficiency for large organizations.

Sai Deepak Reddy Satti

AI Developer, Full Stack Developer, Back-End Developer, +2

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I am an AI/ML engineer based in New York with 4+ years of experience building scalable machine learning, deep learning, generative AI, and multi-agent AI systems. I specialize in production-grade agentic AI applications, RAG pipelines, vector databases, memory-enabled agents, and cloud-native AI architectures. I enjoy turning research into robust, observable AI solutions that automate complex workflows and improve automation and decision-making at enterprise scale. Throughout my career at Scale AI and IBM, I have designed and deployed enterprise-grade AI platforms, tuned LLMs, and led end-to-end ML/Ops pipelines. I thrive in collaborative, cross-functional teams and am passionate about building systems that enhance automation, reasoning, and operational efficiency for large organizations.…I am an AI/ML engineer based in New York with 4+ years of experience building scalable machine learning, deep learning, generative AI, and multi-agent AI systems. I specialize in production-grade agentic AI applications, RAG pipelines, vector databases, memory-enabled agents, and cloud-native AI architectures. I enjoy turning research into robust, observable AI solutions that automate complex workflows and improve automation and decision-making at enterprise scale. Throughout my career at Scale AI and IBM, I have designed and deployed enterprise-grade AI platforms, tuned LLMs, and led end-to-end ML/Ops pipelines. I thrive in collaborative, cross-functional teams and am passionate about building systems that enhance automation, reasoning, and operational efficiency for large organizations.

Available to hire

I am an AI/ML engineer based in New York with 4+ years of experience building scalable machine learning, deep learning, generative AI, and multi-agent AI systems. I specialize in production-grade agentic AI applications, RAG pipelines, vector databases, memory-enabled agents, and cloud-native AI architectures. I enjoy turning research into robust, observable AI solutions that automate complex workflows and improve automation and decision-making at enterprise scale.

Throughout my career at Scale AI and IBM, I have designed and deployed enterprise-grade AI platforms, tuned LLMs, and led end-to-end ML/Ops pipelines. I thrive in collaborative, cross-functional teams and am passionate about building systems that enhance automation, reasoning, and operational efficiency for large organizations.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Agentic AI Engineer at Scale AI

May 1, 2025 - Present

Architected and deployed enterprise-scale Multi-Agent Systems using LangGraph, CrewAI, AutoGen, OpenAI Assistants API, MCP, and LangChain to enable autonomous task planning, tool orchestration, and AI automation workflows, reducing manual operations by 70%. Built production-grade agentic AI applications leveraging GPT-4o, Claude, Llama 3, Mistral, and Transformers to improve task completion accuracy by 35%. Designed advanced RAG pipelines with LlamaIndex, LangChain, Pinecone, FAISS, vector databases, semantic search, and re-ranking techniques, increasing knowledge retrieval precision by 45%. Implemented autonomous agents with function calling, tool calling, API orchestration, code execution, browser automation, and workflow chaining, cutting operational processing time by 60%. Developed memory-enabled agents using vector memory, episodic memory, ReAct, chain-of-thought, self-reflection loops, and structured outputs, improving multi-step reasoning by 40%.

ML & Deep Learning Engineer at IBM

April 1, 2020 - November 1, 2023

Designed and deployed end-to-end ML and DL solutions using Python, PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, and LightGBM, improving predictive model accuracy by 28%. Built NLP applications leveraging Transformers, BERT, RoBERTa, T5, and GPT-based models, increasing document processing efficiency by 50%. Developed computer vision models with OpenCV, YOLO, and CLIP, boosting automated image processing accuracy by 35%. Engineered large-scale data pipelines with Apache Spark, Airflow, Kafka, Pandas, NumPy, and SQL, enabling terabytes of data processing daily. Led distributed training with PyTorch DDP, achieving ~30% KPI improvement. Deployed real-time inference services via FastAPI/Flask, supporting low-latency predictions. Implemented MLOps with MLflow, model versioning, CI/CD, and automated retraining, reducing deployment cycles by 45%. Leveraged cloud platforms (AWS SageMaker, EC2, Lambda, S3; Azure ML; Vertex AI) for cloud-native deployments and applied model optimization tec