Back
Metro location and a senior software title increase competition, but niche agentic AI specialization moderates density.
Role demands specialized agentic AI, RAG, and production ML experience, limiting easy cross-industry transferability.
Explicit 8+ years plus mandatory production agentic AI, RAG, cloud, and observability skills make shortlisting strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and scale enterprise-grade multi-agent AI systems focusing on agent orchestration, context engineering, and production-grade AI that automate business workflows.
Own the full lifecycle including intake, design, validation, deployment, monitoring, improvement, and ensuring observability, evaluation, guardrails, and reliability.
Integrate AI solutions into enterprise platforms (Teams, web apps) with backend services and APIs, ensuring compliance, responsible AI, cost/performance optimization, and driving business impact through PoCs and scaling.
8–10+ years of software engineering experience with strong AI/ML exposure.
Bachelor’s or Master’s degree in Computer Science.
Proficiency in Python, modern backend engineering, API/service development, cloud-native AI service deployment (AWS, Azure, or GCP).
Experience with orchestration frameworks (CrewAI, LangGraph, AutoGen, MAF or equivalent), RAG pipelines, vector search, observability tools (Application Insights, OpenTelemetry, Azure Monitor, New Relic or equivalent), and production-grade agentic AI systems.
Experienced with end-to-end ownership of complex agentic AI platforms integrating multiple agents and context management workflows.
Strong in backend and cloud-native AI system design capable of scalable enterprise integration and monitoring.
Familiar with advanced AI orchestration concepts such as Model Context Protocol (MCP) and agent-to-agent communication for distributed systems.