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Mid-level, metro-based AI Engineer with broad LLM and backend requirements increases applicant competition.
Specialized LLMs, agent frameworks, RAG, and vector DB expertise produces high domain specificity and low transferability.
Explicit 4+ years plus many mandatory AI, backend, and vector-database skills makes shortlisting highly strict.
Design and build agentic and multi-agent AI systems for complex business workflows, including backend services and AI-powered application APIs.
Develop and maintain RAG pipelines, retrieval systems, and enterprise knowledge platforms with evaluation frameworks for quality, reliability, safety, latency, and cost.
Establish monitoring, tracing, security, and governance practices for production AI services and collaborate with stakeholders to deliver impactful AI solutions.
Minimum 4 years of software engineering and/or AI engineering experience.
Proven experience delivering production-grade software and AI applications involving multi-step agentic workflows, tool usage, orchestration, memory, and fault handling.
Strong proficiency in Python and FastAPI with experience in asynchronous programming, API development, containerized applications, LLM-based applications, RAG architectures, vector databases, and model evaluation.
Familiarity with agent frameworks (e.g., Google ADK, LangChain, LangGraph, DeepAgents, AutoGen, CrewAI) and experience deploying secure, scalable applications in cloud environments.
Experienced in architecting and implementing multi-agent AI workflows with operational reliability and fault tolerance.
Skilled in working across the full AI application lifecycle including architecture, backend development, deployment, monitoring, and optimization.
Comfortable evaluating and integrating latest AI models, tools, and frameworks within enterprise-scale AI environments and cloud platforms.