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Mid-level ML role with metro location and common SDE title but specialized AI skills reduce applicant density.
Role requires specialized ML/Generative AI and vector-search experience, limiting cross-industry transferability.
Explicit 6–9 years requirement plus mandatory ML/RAG/vector search production skills increases filtering intensity.
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Design and implement agentic AI systems featuring multi-agent communication, role collaboration, and multi-step workflow orchestration.
Build and maintain evaluation frameworks for agent workflows, retrieval quality, reasoning consistency, and output reliability with automated methods.
Develop memory and context management, optimize Retrieval-Augmented Generation (RAG) pipelines, and enhance AI system performance using statistical and ML techniques.
6–9 years of professional software engineering experience including meaningful AI and ML exposure.
Strong Python skills including AsyncIO, multiprocessing, and backend frameworks like FastAPI or Flask.
Experience in AI systems covering agentic AI, retrieval systems, embeddings, vector search, and evaluation metrics such as Recall@K, MRR, and NDCG.
Work Experience Required: 6–9 years in software engineering focused on AI/ML systems.
Proven capability to operate across system design, implementation, debugging, performance optimization, and documentation in AI-focused projects.
Experienced with agentic AI concepts such as graph-based orchestration, tool-calling patterns, and Model Context Protocol (MCP).
Skilled in developing production-ready AI services, comfortable collaborating with product and engineering teams to deliver scalable AI infrastructure.