Databricks AI Engineer Amazon Bedrock AgentCore

We are seeking an experienced Senior Generative AI Engineer with strong hands-on expertise in Amazon Bedrock AgentCore, AWS Bedrock, and Databricks to design, develop, and deploy enterprise-grade Generative AI and agentic AI solutions. The ideal candidate will have a strong background in cloud-native application development, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, data engineering, and enterprise system integration. The candidate will work closely with architecture, data, security, DevOps, and application teams to build scalable, secure, and production-ready AI solutions. Key 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.

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. Databricks 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. Knowledge of Databricks AI/agent development capabilities is highly desirable. AWS 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

Programming Skills

• 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.
• Familiarity with agent frameworks such as LangGraph, LangChain, CrewAI, Strands Agents, or similar frameworks is preferred.

AI / GenAI Experience Candidate should have practical experience with:

• 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

DevOps / Deployment

• Experience with CI/CD pipelines.
• Experience with Git/GitHub or equivalent source-control platforms.
• Experience with Docker and containerized applications.
• Experience with Infrastructure as Code using Terraform or AWS CloudFormation is preferred.
• 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.

Preferred Qualifications

• 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.