We are seeking a highly skilled Senior AI Engineer with strong expertise in Kubernetes and vector database technologies to join our team. In this role, you will design, build, and scale production-grade AI systems, working with cutting-edge LLM frameworks, embeddings, and cloud-native infrastructure to deliver robust and high-performance solutions. Responsibilities Deploy and manage Milvus vector databases, including schema design and index tuning (HNSW, IVF-FLAT) Build and maintain embedding and LLM pipelines using OpenAI API, Hugging Face, or Cohere Manage Kubernetes clusters, Helm charts, and containerized microservices in production Develop and maintain Docker containerization workflows, including multi-stage builds and registry management Design and deliver production-grade Python applications, integrating with Go, Java, or C++ where required Integrate object storage systems such as AWS S3, MinIO, or Google Cloud Storage Evaluate and implement alternative vector database solutions, including Qdrant, Pinecone, and Weaviate Collaborate cross-functionally with team members to deliver reliable, scalable AI services Ensure operational excellence, observability, and performance of deployed AI workloads Requirements Bachelor's degree in Engineering with 5+ years of relevant experience Expertise in Milvus deployment, schema design, and index tuning (HNSW, IVF-FLAT) Familiarity with vector database alternatives such as Qdrant, Pinecone, Weaviate, PGVector, or Chroma Proficiency in building embedding and LLM pipelines using OpenAI API, Hugging Face, or Cohere Skills in Kubernetes cluster management, Helm charts, and containerized microservices Background in Docker containerization, multi-stage builds, and registry management Production-level Python development along with Go, Java, or C++ Knowledge of object storage integration, including AWS S3, MinIO, or Google Cloud Storage Excellent verbal and written communication skills with strong team collaboration abilities Proficiency in English at an Upper-Intermediate level (B2) or higher Nice to have Experience supporting large-scale RAG applications and multi-agent platforms Hands-on familiarity with LangChain, LlamaIndex, or custom pipelines Understanding of GPU scheduling, resource optimization, and inference acceleration Production experience with hybrid search, metadata filtering, and index tuning Implementation of LLM evaluation, governance, tracing, and monitoring tools Familiarity with CI/CD pipelines, Infrastructure-as-Code, and cloud-native deployment practices Prior work experience in the Oil and Gas industry Experience with Dataiku DSS Knowledge of SRE practices