Position - Senior Data Scientist / Agentic AI Engineer
Location - 100% Remote
Experience - 10 Year
W2 Contract Only
Job Description- We are seeking a highly experienced Data Scientist with deep expertise in Agentic AI, Large Language Models (LLMs), Machine Learning Operations (MLOps), and intelligent automation.
Key Responsibilities:
Design, develop, and deploy end-to-end Agentic AI solutions capable of autonomous decision-making and task execution.
Build AI agents that interact with enterprise systems, databases, APIs, message queues (MQ), servers, and operational platforms.
Develop intelligent workflows to automate business processes and eliminate manual human intervention.
Create AI-powered solutions for operational use cases such as lane denial management, exception handling, workflow orchestration, incident resolution, and process optimization.
Implement scalable MLOps practices for model deployment, monitoring, governance, and continuous improvement.
Integrate LLMs, Retrieval-Augmented Generation (RAG), multi-agent frameworks, and enterprise data sources.
Collaborate with business and technology teams to identify automation opportunities and transform manual processes into autonomous AI-driven operations.
Required Skills:
Strong experience with Agentic AI frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, etc.).
Expertise in LLMs, Prompt Engineering, RAG, Vector Databases, and AI Orchestration.
Experience with Python, Machine Learning, Deep Learning, and Data Science.
Hands-on knowledge of MLOps, CI/CD pipelines, model monitoring, and cloud platforms.
Experience working with Message Queues (MQ), APIs, enterprise servers, distributed systems, and workflow automation platforms.
Strong problem-solving skills with the ability to design autonomous systems that minimize or eliminate manual intervention.
Preferred Experience:
Supply Chain, Logistics, Transportation, or Operations Automation use cases.
Real-time decisioning systems and AI-driven workflow automation.
Multi-agent architectures and enterprise AI deployment at scale.