AI Practice Lead
Tasks
- Advise stakeholders on AI transformation and agent adoption
- Build AI agent design orchestration and deployment frameworks
- Build mentor and lead AI engineering and data teams
- Define AI practice vision roadmap and operating model
- Define reference architectures for agentic AI GenAI and MLOps
- Design and develop LLM-based AI agents
- Ensure production reliability observability and performance
- Ensure scalability security and compliance
- Establish AI standards governance security and responsible AI
- Evaluate and prioritize AI agent use cases
- Implement multi agent systems tool using agents and workflow orchestration
- Integrate AI agents with enterprise systems via APIs databases and CRM
- Lead AI workshops discovery sessions and agent based PoCs
- Own end to end delivery of AI solutions from PoC to production
- Perform prompt engineering RAG fine tuning and evaluation
- Provide technical direction and solution review
- Support proposals RFPs and solution presentations
- Upskill teams on AI literacy and agent frameworks
Perks/Benefits
- N/A
Skills/Tech-stack
API Integration | Agent systems | Agentic Systems | Artificial Intelligence | Autogen | CrewAI | Data Science | Embeddings | Fine Tuning | Generative AI | LLMOps | LLMs | Langchain | Llamaindex | MLOps | Machine Learning | Multi-Agent | Multi-Agent Systems | Observability | Privacy | Prompt engineering | RAG Architectures | Responsible AI | Retrieval-Augmented Generation | Security | Vector Databases | Workflow Orchestration
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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