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Specialized LLM/agentic skillset reduces mass applicants but mid-level and attractive domain sustain moderate competition.
Requires niche agentic AI, LLM, and cloud data infrastructure expertise, limiting easy cross-industry transfers.
Many mandatory AWS, data engineering, LLM/agent, and 5-10 year experience requirements increase filter strictness.
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Design and implement autonomous agentic AI systems for self-orchestrating data workflows and decision pipelines
Build and optimize scalable data pipelines and AI/ML applications using AWS AI/ML services, LLM-powered retrieval-augmented generation, and agent frameworks
Ensure data quality, governance, security, and cost-performance optimization of cloud-based AI workloads while collaborating with interdisciplinary teams
5-10 years total experience
Bachelor's or Master's degree in Computer Science, Engineering, or related field
Strong experience with AWS services including S3, Glue, Lambda, Step Functions, Redshift/Athena, SageMaker, and Bedrock
Proficiency in Python, SQL, ETL/ELT tools, distributed processing (Spark/PySpark), streaming tech (Kafka/Kinesis), and familiarity with LLMs, agent frameworks, vector databases, and data lakehouse architectures
Experienced in building and deploying autonomous AI agents and multi-agent collaboration systems in enterprise environments
Deep expertise integrating LLMs and generative AI systems with cloud-native data platforms and container orchestration (Docker, Kubernetes)
Skilled in designing production-grade AI/ML pipeline architectures with strong emphasis on monitoring, observability, and MLOps/LLMOps best practices