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About the role
Role Overview: You will be responsible for designing and engineering agentic AI systems, GenAI engineering, classical ML and predictive intelligence, MCP, tools, and integrations, MLOps, infrastructure, and open-source stack on AWS, as well as promoting a vibe coding and engineering culture. Additionally, you will play a key role in shaping the company-wide AI strategy and leadership. Key Responsibilities: - Architect and build multi-agent systems that streamline core workflows such as RFQ generation, supplier discovery, price benchmarking, BOM parsing, and inventory matching. - Design agent graphs using frameworks like LangGraph, CrewAI, or AutoGen, defining agent roles, tool registries, state machines, escalation paths, and human-in-the-loop checkpoints. - Build and maintain MCP servers to expose internal data and business logic for consumption by AI agents and external clients. - Develop RAG pipelines for datasheet extraction, BOM parsing, RFQ drafting, and supplier communication, grounded in proprietary component data. - Fine-tune open-source LLMs for domain-specific tasks and build prompt engineering systems for reproducibility. - Implement classical ML models for various tasks like pricing signal detection, demand forecasting, and inventory risk assessment. - Design and maintain the MCP server layer for callable tools, define tool taxonomy, and build integration connectors for distributor APIs and ERP systems. - Own the end-to-end MLOps stack on AWS, prefer open-source tooling, and ensure model monitoring and cost discipline. - Actively use and champion AI-assisted development tools, guide the Vibe Coder, and drive a culture of shipping products efficiently. - Define the AI roadmap, embed AI capabilities into core product workflows, champion responsible AI, and recruit, mentor, and develop team members. Qualifications Required: - Strong coding skills, especially in Python, with a focus on hands-on engineering and technical leadership. - 11+ years of experience in software engineering, data science, ML, or AI, with at least 5 years leading production AI/ML systems. - Proficiency in agentic AI, GenAI engineering, classical ML, AWS services, open-source tools, and vibe coding. - Excellent communication skills to explain technical concepts to both technical and non-technical stakeholders. Please note that the preferred qualifications are not included in this summary. Role Overview: You will be responsible for designing and engineering agentic AI systems, GenAI engineering, classical ML and predictive intelligence, MCP, tools, and integrations, MLOps, infrastructure, and open-source stack on AWS, as well as promoting a vibe coding and engineering culture. Additionally, you will play a key role in shaping the company-wide AI strategy and leadership. Key Responsibilities: - Architect and build multi-agent systems that streamline core workflows such as RFQ generation, supplier discovery, price benchmarking, BOM parsing, and inventory matching. - Design agent graphs using frameworks like LangGraph, CrewAI, or AutoGen, defining agent roles, tool registries, state machines, escalation paths, and human-in-the-loop checkpoints. - Build and maintain MCP servers to expose internal data and business logic for consumption by AI agents and external clients. - Develop RAG pipelines for datasheet extraction, BOM parsing, RFQ drafting, and supplier communication, grounded in proprietary component data. - Fine-tune open-source LLMs for domain-specific tasks and build prompt engineering systems for reproducibility. - Implement classical ML models for various tasks like pricing signal detection, demand forecasting, and inventory risk assessment. - Design and maintain the MCP server layer for callable tools, define tool taxonomy, and build integration connectors for distributor APIs and ERP systems. - Own the end-to-end MLOps stack on AWS, prefer open-source tooling, and ensure model monitoring and cost discipline. - Actively use and champion AI-assisted development tools, guide the Vibe Coder, and drive a culture of shipping products efficiently. - Define the AI roadmap, embed AI capabilities into core product workflows, champion responsible AI, and recruit, mentor, and develop team members. Qualifications Required: - Strong coding skills, especially in Python, with a focus on hands-on engineering and technical leadership. - 11+ years of experience in software engineering, data science, ML, or AI, with at least 5 years leading production AI/ML systems. - Proficiency in agentic AI, GenAI engineering, classical ML, AWS services, open-source tools, and vibe coding. - Excellent communication skills to explain technical concepts to both technical and non-technical stakeholders. Please note that the preferred qualifications are not included in this summary.