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Strong employer brand but specialized agentic GenAI expertise and seniority narrow the applicant pool.
Medium because advanced GenAI and vector DB skills transfer across industries, yet agentic systems expertise is specialized.
High due to explicit 7+ years and many mandatory GenAI, cloud, and vector DB technology requirements.
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Architect and build production-grade autonomous AI workflows involving multi-agent systems with complex state management and agent-to-agent communication.
Lead engineering of data pipelines for real-time context injection; bridge data engineering and AI teams to ensure operational readiness of agent systems.
Define and enforce technical standards, lead roadmap for agentic AI architecture, and prototype innovative autonomous AI solutions.
7+ years technical experience in Software Engineering, Data Engineering, or Machine Learning.
2+ years experience building and deploying LLM-based applications or Agentic Systems in production.
Proficiency with cloud infrastructure and container orchestration (AWS/GCP/Azure, Kubernetes, Docker) and advanced Python programming skills.
Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field.
Experienced architect capable of designing scalable, stateful multi-agent AI systems integrating complex messaging and memory management.
Comfortable operating at the intersection of software engineering, data engineering, and machine learning, especially with vector databases and LLM frameworks.
Proven technical leader able to translate strategic AI roadmap into hands-on deliverables, mentor engineers, and foster innovation in autonomous agentic applications.