Artificial Intelligence Engineer

 AI/ML Role with Agentic AI Exposure – Key Elements

 Core Responsibilities

• Design and implement agentic AI systems that autonomously plan and execute tasks to meet user-defined goals. Very focused on python code generation .
• Develop and deploy LLM-based agents using frameworks like LangChain, LangGraph, and custom runtimes. 
• Integrate AI agents with external APIs and services, enabling orchestration across enterprise workflows. 
• Build and maintain pipelines for training, testing, and deploying models in production environments (e.g., Azure ML).

 Technical Skills Required

• Programming: Python, SQL, PySpark,
• Frameworks & Tools: LangChain, LangGraph,HuggingFace Transformers, OpenAI API, databricks
• Cloud Platforms: Azure,
• ML/AI Techniques: Reinforcement learning, multimodal models, vector databases (e.g., FAISS, Pinecone), NLP. 
• DevOps: CI/CD pipelines, Docker, Kubernetes, Airflow.

 Agentic AI Exposure

• Agentic AI refers to autonomous or semi-autonomous software entities that perceive, decide, and act to achieve goals in digital or physical environments.
• Roles often involve building multi-agent systems, enabling emergent behaviors, and ensuring robustness and trustworthiness in agent actions. 
• Use cases Generate Python code from business rules and RAG, include automating workflows, generating reports,

 Qualifications

• Bachelor's or Master’s degree in Computer Science, Data Science, Engineering, or related field.
• 3–10 years of experience in AI/ML, with at least 2 years in agentic AI or autonomous systems.
• Experience in deploying production-grade AI solutions and working with enterprise data systems.

• What they are trying to do is leverage GitHub and CoPilot and they need someone with Advanced Github and CoPilot knowledge
• Need someone that is ahead of them in that direction.
• Looking for a mentor
• Problem:  Plugin for integration for python project. Code to accelerate. Need a structure the way the engineering is done.