Back
Mid-level popular data role with a mid experience band but specialized LLM/agent skills increases selectivity.
Highly domain-specific LLM, vector DB, and AWS MLOps requirements limit cross-industry portability.
Explicit 5-10 years requirement plus many mandatory cloud, data engineering, and LLM infrastructure skills increases strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build autonomous AI data systems and pipelines using agentic AI frameworks and LLMs.
Integrate and optimize AWS AI/ML services including SageMaker and Bedrock within scalable data architectures (lakes, warehouses, lakehouses).
Collaborate with cross-functional teams to deploy and monitor intelligent AI/ML applications and ensure data quality, governance, and cloud cost-performance optimization.
5-10 years of total experience required.
Strong expertise with AWS services: S3, Glue, Lambda, Step Functions, Redshift, Athena, SageMaker, Bedrock.
Proficiency in Python, SQL, ETL/ELT tools, distributed processing (Spark/PySpark), streaming (Kafka/Kinesis), and data modeling.
Hands-on experience with LLMs, agent frameworks, retrieval-augmented generation (RAG), vector databases, and container orchestration (Docker/Kubernetes).
Experienced in building and deploying autonomous AI agents and multi-agent orchestration systems in enterprise environments.
Skilled at merging data engineering with AI/ML infrastructure, including MLOps/LLMOps practices and graph-based or real-time AI workflows.
Expert in scalable cloud-native architectures that integrate multiple AI services and support evolving agentic AI capabilities.