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Metro location and broad systems skills increase applicants, but senior niche LLM/agent expertise limits candidate pool.
Role demands specialized LLM, agent, and ML systems experience, making cross-industry transferability low.
Explicit 10+ years and 3+ years AI/ML plus many mandatory technical requirements imply strict shortlisting.
Architect, implement, and optimize enterprise-scale AI-driven systems, including LLMs, RAG pipelines, and multi-agent architectures.
Operationalize and manage agent lifecycle ensuring reliability, security, autonomy guardrails, and compliance with Responsible AI standards.
Collaborate cross-functionally to integrate AI features into products, driving measurable business value and continuous innovation.
10+ years software development experience, including 3+ years specifically in AI/ML systems with LLMs, RAG, and agent-based architectures.
Expert proficiency in Python, C#, Angular/JavaScript; experience with AI tooling frameworks like MCP, A2A/ADK, LangChain, CrewAI, AutoGen.
Strong knowledge of cloud platforms (AWS preferred, Azure or GCP also acceptable) and cloud-native services for LLM workloads.
Familiarity with event-driven architectures, distributed systems, message brokers (Kafka, RabbitMQ, Solace PubSub+), and DevOps practices such as CI/CD, Docker, and infrastructure-as-code.
A systems-oriented AI engineer with deep understanding of distributed, event-driven architectures and cloud infrastructure tailored for AI workloads.
Experienced in deploying and maintaining enterprise-scale multi-agent systems with a strong focus on Responsible AI governance and operational security.
Able to strategize AI platform evolution by collaborating effectively across product, research, and engineering teams to translate business needs into scalable AI solutions.