[Remote] Databricks AI Engineer
Note: The job is a remote job and is open to candidates in USA. Dice is seeking an experienced Senior Generative AI Engineer to design, develop, and deploy enterprise-grade Generative AI and agentic AI solutions. The role focuses on Amazon Bedrock AgentCore, AWS Bedrock, Databricks, AI agents, RAG architectures, data engineering, cloud-native development, and enterprise system integration.
Responsibilities
- Design and develop enterprise Generative AI and Agentic AI applications using Amazon Bedrock and Amazon Bedrock AgentCore
- Build and deploy AI agents capable of reasoning, tool calling, workflow execution, and integration with enterprise systems
- Implement Amazon Bedrock AgentCore Runtime for secure and scalable deployment of AI agents
- Implement AgentCore capabilities including Memory, Gateway, Identity, and Observability
- Develop multi-agent and autonomous agent workflows for complex business processes
- Integrate AI agents with REST APIs, AWS Lambda, databases, enterprise applications, and external services
- Design and implement Retrieval-Augmented Generation (RAG) architectures using enterprise data
- Work with Amazon Bedrock foundation models and develop prompt engineering and model orchestration strategies
- Implement security controls using IAM, authentication, authorization, encryption, and enterprise identity management
- Configure monitoring, logging, tracing, and operational dashboards for GenAI applications
- Develop data pipelines and AI/ML workflows using Databricks
- Build scalable data-processing solutions using Apache Spark and PySpark
- Develop and manage data using Delta Lake and Unity Catalog
- Build AI and GenAI applications leveraging Databricks capabilities for agent development, model serving, evaluation, and monitoring
- Use MLflow for experiment tracking, model lifecycle management, GenAI tracing, and evaluation
- Integrate Databricks data platforms with AWS Bedrock-based GenAI applications
- Develop APIs and backend services using Python
- Implement CI/CD pipelines for AI applications, models, agents, and infrastructure
- Collaborate with data engineers, cloud architects, security teams, business analysts, and product teams
- Conduct performance tuning, cost optimization, security reviews, and production troubleshooting
- Develop technical documentation, architecture diagrams, deployment procedures, and operational support documentation
Skills
- Strong hands-on experience with Amazon Bedrock
- Experience developing Generative AI applications using foundation models
- Experience with Amazon Bedrock AgentCore or similar enterprise agent runtime platforms
- Understanding of: AgentCore Runtime AgentCore Memory AgentCore Gateway AgentCore Identity AgentCore Observability
- Experience designing AI agent and multi-agent architectures
- Experience with tool calling and API integration
- Experience with RAG architectures
- Experience with prompt engineering and LLM orchestration
- Knowledge of Bedrock Knowledge Bases, Guardrails, and enterprise AI security concepts
- Strong hands-on experience with Databricks
- Strong experience with: Apache Spark PySpark Delta Lake Unity Catalog MLflow Databricks Workflows
- Experience developing AI/ML or Generative AI solutions on Databricks
- Experience with model serving, model evaluation, monitoring, and lifecycle management
- Experience integrating Databricks with AWS services and enterprise applications
- Strong experience with AWS services such as:
- * Amazon Bedrock
- * AWS Lambda
- * Amazon S3
- * Amazon API Gateway
- * Amazon CloudWatch
- * AWS IAM
- * AWS Secrets Manager
- * Amazon VPC
- * AWS KMS
- * Amazon ECR
- * Amazon ECS/EKS or other container platforms
- Strong programming experience with Python
- Experience developing REST APIs and microservices
- Experience with Python AI/ML and LLM libraries
- Experience with SQL
- Experience working with JSON, REST APIs, OAuth, and enterprise integration patterns
- Large Language Models (LLMs)
- Generative AI
- Agentic AI
- AI Agents
- Multi-Agent Systems
- Retrieval-Augmented Generation (RAG)
- Vector databases / Vector Search
- Embeddings
- Prompt Engineering
- Tool Calling
- Function Calling
- AI Model Evaluation
- LLM Observability
- Responsible AI and Guardrails
- Experience with CI/CD pipelines
- Experience with Git/GitHub or equivalent source-control platforms
- Experience with Docker and containerized applications
- Experience deploying production-grade AI/ML applications in AWS environments
- Understanding of monitoring, logging, tracing, resiliency, scalability, and disaster recovery
- * Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field
- * 8+ years of overall IT/software engineering experience.
- * 3+ years of cloud/data/AI engineering experience.
- * Strong hands-on experience with AWS and Databricks
- * Demonstrated experience designing and delivering enterprise-scale AI or Generative AI solutions
- * Strong analytical, troubleshooting, communication, and problem-solving skills
- * Ability to work with business and technical stakeholders to translate business requirements into scalable technical solutions
- Familiarity with agent frameworks such as LangGraph, LangChain, CrewAI, Strands Agents, or similar frameworks is preferred
- Experience with Infrastructure as Code using Terraform or AWS CloudFormation is preferred
- * AWS certifications
- * Databricks certifications
- * Experience building production-grade AI agents
- * Experience integrating AI solutions with enterprise systems such as Salesforce, SAP, ServiceNow, or similar platforms
- * Experience working in regulated enterprise environments
- * Experience with security, governance, auditability, and Responsible AI practices
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