Architect and implement scalable data and AI systems, including ETL/ELT pipelines, streaming (Kafka), and diverse database technologies (relational, NoSQL, graph, vector).
Develop and operationalize LLM and multi-agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI) with robust lifecycle management and safe execution practices.
Design and manage CI/CD pipelines for models and applications using GitHub/GitLab, with deployments on Kubernetes and cloud platforms.
Ensure security and compliance by applying AI governance frameworks, implementing LLM guardrails, and following DevOps best practices.
Collaborate cross-functionally, lead incident response, mentor engineers, and communicate technical solutions effectively to stakeholders.
Essential Requirements
Proficient in Python and SQL, with additional experience in .NET, C#, Java, Scala, or C++ for backend development and data processing.
Expert in data engineering tools and techniques, including ETL, Kafka streaming, Airflow orchestration, and modeling across relational, NoSQL, graph (Neo4j), and vector databases (e.g., pgvector, Milvus).
Skilled in cloud-native development, especially on Azure, with hands-on experience in Docker, Kubernetes (including GPU orchestration), and Infrastructure as Code (Terraform/Bicep).
Experienced in ML operations and CI/CD, using tools like MLflow or Kubeflow, GitHub/GitLab pipelines, model registries, drift monitoring, and automated retraining workflows.
Strong communication with a solid grasp of software engineering best practices, documentation standards, and system maintenance.
Desirable Requirements
Experience with Agile development methodology (e.g., Scrum)