Qode
Kafka and GenAI Infrastructure Engineer
Qode - Fort Mill, South Carolina, United States, 29715
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Overview
Job Title: Kafka and GenAI Infrastructure Engineer Location: Fort Mill SC, Jersey City New Jersey (Hybrid) Duration: Full-time Job Summary: Incedo is seeking a highly skilled Kafka and GenAI Infrastructure Engineer to join our cutting-edge platform and data engineering team. This role involves designing, building, and maintaining scalable, secure infrastructure solutions for Apache Kafka and integrating them with Generative AI (GenAI) workloads in a cloud-native environment. Key Responsibilities:
Kafka Engineering & Operations: Design, deploy, and manage scalable Apache Kafka / Confluent Kafka clusters. Handle Kafka topics, brokers, schema registry, connectors (Kafka Connect), and stream processing (Kafka Streams/KSQL). Implement monitoring, alerting, and log aggregation for Kafka ecosystem (using tools like Prometheus, Grafana, Splunk, etc.). Ensure high availability, fault tolerance, and disaster recovery of Kafka clusters. GenAI Infrastructure: Work with LLMs and vector databases to support GenAI use cases. Integrate Kafka with AI/ML pipelines for real-time inference and data streaming to/from GenAI models. Deploy and manage GenAI services (e.g., OpenAI, Hugging Face, Vertex AI, Amazon Bedrock) within secure, compliant infrastructure. Cloud & DevOps Enablement: Provision infrastructure using Terraform / CloudFormation / Pulumi. Manage deployments in AWS, Azure, or GCP using Kubernetes / ECS / Lambda. Build CI/CD pipelines (GitLab/GitHub Actions/Jenkins) for Kafka and AI/ML components. Security & Compliance: Ensure encryption, RBAC, and data protection policies across Kafka and GenAI workloads. Collaborate with InfoSec and Governance teams to ensure compliance with industry regulations. Required Skills:
5+ years of experience with Apache Kafka / Confluent Platform 3+ years of experience with cloud infrastructure (AWS, Azure, or GCP) Familiarity with GenAI / LLM integration (OpenAI, LangChain, vector stores) Strong understanding of stream processing and event-driven architecture Experience with Infrastructure as Code (IaC) and CI/CD pipelines Experience with containerization tools: Docker, Kubernetes Proficiency in scripting: Python, Bash, or Go Preferred Qualifications:
Experience integrating Kafka with AI/ML platforms or MLOps tools Familiarity with DataBricks, Apache Flink, or Spark Structured Streaming Exposure to data governance and lineage tools (e.g., Apache Atlas, Collibra) Financial industry experience is a plus Education:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
Kafka Engineering & Operations: Design, deploy, and manage scalable Apache Kafka / Confluent Kafka clusters. Handle Kafka topics, brokers, schema registry, connectors (Kafka Connect), and stream processing (Kafka Streams/KSQL). Implement monitoring, alerting, and log aggregation for Kafka ecosystem (using tools like Prometheus, Grafana, Splunk, etc.). Ensure high availability, fault tolerance, and disaster recovery of Kafka clusters. GenAI Infrastructure: Work with LLMs and vector databases to support GenAI use cases. Integrate Kafka with AI/ML pipelines for real-time inference and data streaming to/from GenAI models. Deploy and manage GenAI services (e.g., OpenAI, Hugging Face, Vertex AI, Amazon Bedrock) within secure, compliant infrastructure. Cloud & DevOps Enablement: Provision infrastructure using Terraform / CloudFormation / Pulumi. Manage deployments in AWS, Azure, or GCP using Kubernetes / ECS / Lambda. Build CI/CD pipelines (GitLab/GitHub Actions/Jenkins) for Kafka and AI/ML components. Security & Compliance: Ensure encryption, RBAC, and data protection policies across Kafka and GenAI workloads. Collaborate with InfoSec and Governance teams to ensure compliance with industry regulations. Required Skills:
5+ years of experience with Apache Kafka / Confluent Platform 3+ years of experience with cloud infrastructure (AWS, Azure, or GCP) Familiarity with GenAI / LLM integration (OpenAI, LangChain, vector stores) Strong understanding of stream processing and event-driven architecture Experience with Infrastructure as Code (IaC) and CI/CD pipelines Experience with containerization tools: Docker, Kubernetes Proficiency in scripting: Python, Bash, or Go Preferred Qualifications:
Experience integrating Kafka with AI/ML platforms or MLOps tools Familiarity with DataBricks, Apache Flink, or Spark Structured Streaming Exposure to data governance and lineage tools (e.g., Apache Atlas, Collibra) Financial industry experience is a plus Education:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field