AI Integration Engineer
Medlytix•Roswell, GA
2d
About The Position
We're seeking an AI Integration Engineer with a strong data engineering foundation to design and build the data infrastructure that powers our AI-driven automation and business intelligence initiatives. This role sits at the intersection of data engineering, API development, and AI, focusing on creating robust data exchange systems and multi-agent AI workflows that replace legacy processes. You'll build systems where AI agents and humans collaborate through bidirectional work delegation-both for our internal operations and the client solutions we deliver.
Requirements
- Data Engineering: 3+ years of experience with modern data stack (dbt, Airflow, Spark, etc.)
- API Development: Strong proficiency in Python and similar languages for API development
- Database Expertise: Deep knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Redis, etc.)
- Cloud Platforms: Experience with AWS, GCP, or Azure data and AI services
- API Technologies: REST, GraphQL, gRPC, WebSockets, message queues (Kafka, RabbitMQ)
- AI/ML Familiarity: Understanding of LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, RAG systems, and vector databases (Pinecone, Weaviate, Chroma)
Nice To Haves
- Experience with AI agent frameworks (LangChain, LlamaIndex, Pydantic AI, AutoGen, CrewAI)
- Knowledge of API management platforms (Kong, Apigee, AWS API Gateway)
- Familiarity with containerization and orchestration (Docker, Kubernetes)
- Experience with event-driven architectures and microservices
- Understanding of data security, encryption, and compliance requirements (GDPR, HIPAA, PHI, etc.)
- Background in DataOps, MLOps, or DevOps practices
- Familiarity with WASM based tools and design patterns.
Responsibilities
- Design, develop, and maintain scalable RESTful for data exchange between systems
- Build real-time data pipelines and streaming architectures to support AI agent operations
- Implement API gateways, rate limiting, authentication, and security best practices
- Create comprehensive API documentation and integration guides for internal and external stakeholders
- Monitor API performance, optimize response times, and ensure high availability
- Integrate and orchestrate AI agents (LLMs, ML models, autonomous agents) into business workflows
- Design and implement agent-to-agent communication protocols and data handoffs
- Build middleware and connectors to enable AI agents to access enterprise data sources
- Develop feedback loops and monitoring systems for AI agent performance
- Create automated workflows that combine multiple AI capabilities (RAG systems, function calling, multi-agent systems)
- Maintain and optimize data warehouses, data lakes, and vector databases
- Build ETL/ELT pipelines that prepare data for AI consumption
- Implement data quality checks, validation, and governance frameworks
- Design data models that support both analytical queries and AI agent operations
- Manage structured and unstructured data storage solutions
- Collaborate with BI teams to expose data through APIs for dashboards and reporting
- Build data APIs that serve real-time metrics and KPIs
- Create data aggregation services that support analytical workloads
- Enable self-service data access through well-documented APIs and MCP servers
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
