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Metro mid-level role at a moderate brand with niche agentic-LLM skills, so moderate competition.
Specialized agentic AI, LLM, and cloud data-infra skills reduce cross-industry transferability.
Explicit 5-10 years plus many mandatory AWS, LLM, and data-infrastructure skills makes shortlisting highly selective.
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Design and implement autonomous agentic AI systems that orchestrate complex data workflows and decision pipelines.
Build and optimize scalable data pipelines and AI/ML infrastructures integrating AWS AI/ML services and large language models.
Develop LLM-powered applications using retrieval-augmented generation, multi-agent frameworks, and expose AI capabilities via APIs and microservices.
5-10 years total experience in data engineering or ML engineering.
Proficiency in Python, SQL, and distributed data processing frameworks (e.g., Spark, PySpark).
Strong hands-on experience with AWS services including S3, Glue, Lambda, Step Functions, Redshift/Athena, SageMaker, and Bedrock.
Experience with LLMs, generative AI, agent frameworks, data lakehouse architectures, vector databases, containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
Experienced in building production-grade autonomous AI systems and multi-agent orchestration platforms within cloud-native AWS environments.
Skilled in integrating advanced AI technologies including retrieval-augmented generation, LLMOps/MLOps practices, and streaming/event-driven architectures.
Capable of designing scalable, self-optimizing data pipelines and AI services collaborating cross-functionally with data scientists and ML engineers in fast-evolving AI settings.