Key Responsibilities

• Design and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines
• Build scalable data pipelines for structured and unstructured data (batch + real-time)
• Develop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks
• Integrate AWS AI/ML services into production-grade architectures
• Develop and optimize data lakes, warehouses, and lakehouse architectures
• Build APIs and microservices to expose AI/ML capabilities
• Ensure data quality, governance, and security across pipelines
• Collaborate with data scientists, ML engineers, and product teams to deploy AI solutions
• Implement monitoring, logging, and observability for AI agents and pipelines
• Optimize cost and performance of cloud-based AI workloads

Required Technical Skills

Cloud & AWS Ecosystem

• Strong experience with AWS services, including:
  • Amazon S3, Glue, Lambda, Step Functions
  • Amazon Redshift / Athena
  • Amazon SageMaker (training, deployment, pipelines)
  • Amazon Bedrock (foundation models, agents, knowledge bases)

AI/ML & Agentic Systems

• Experience with LLMs and generative AI systems
• Hands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems)
• Familiarity with AgentCore / agent orchestration platforms
• Understanding of RAG architectures, embeddings, and vector databases
• Experience with model deployment, inference optimization, and prompt engineering

Data Engineering

• Strong proficiency in Python and SQL
• Experience with ETL/ELT tools and frameworks
• Distributed data processing (Spark, PySpark, or similar)
• Streaming technologies (Kafka, Kinesis, or similar)
• Data modeling and schema design

Data & AI Infrastructure

• Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch)
• Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi)
• Containerization (Docker) and orchestration (Kubernetes)
• CI/CD for ML and data pipelines

Key Responsibilities

• Design and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines
• Build scalable data pipelines for structured and unstructured data (batch + real-time)
• Develop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks
• Integrate AWS AI/ML services into production-grade architectures
• Develop and optimize data lakes, warehouses, and lakehouse architectures
• Build APIs and microservices to expose AI/ML capabilities
• Ensure data quality, governance, and security across pipelines
• Collaborate with data scientists, ML engineers, and product teams to deploy AI solutions
• Implement monitoring, logging, and observability for AI agents and pipelines
• Optimize cost and performance of cloud-based AI workloads

Required Technical Skills

Cloud & AWS Ecosystem

• Strong experience with AWS services, including:
  • Amazon S3, Glue, Lambda, Step Functions
  • Amazon Redshift / Athena
  • Amazon SageMaker (training, deployment, pipelines)
  • Amazon Bedrock (foundation models, agents, knowledge bases)

AI/ML & Agentic Systems

• Experience with LLMs and generative AI systems
• Hands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems)
• Familiarity with AgentCore / agent orchestration platforms
• Understanding of RAG architectures, embeddings, and vector databases
• Experience with model deployment, inference optimization, and prompt engineering

Data Engineering

• Strong proficiency in Python and SQL
• Experience with ETL/ELT tools and frameworks
• Distributed data processing (Spark, PySpark, or similar)
• Streaming technologies (Kafka, Kinesis, or similar)
• Data modeling and schema design

Data & AI Infrastructure

• Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch)
• Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi)
• Containerization (Docker) and orchestration (Kubernetes)
• CI/CD for ML and data pipelines

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 4+ years of experience in data engineering or ML engineering
• Hands-on experience with production-grade AI/ML systems

This position may pay a base salary of up to $150k per year based on skills and experience.