[Hiring] MLOps & AIOps / GCP Engineer @Sparix Global.

Role Description

We are seeking an experienced MLOps & AIOps / GCP Engineer to join the Gemini domain team. This role focuses on designing, building, and operating secure, scalable, production-grade ML and GenAI platforms on Google Cloud Platform (GCP), enabling reliable model training, deployment, monitoring, and incident response. You will collaborate with data scientists, platform engineers, and product teams to operationalize ML/GenAI use cases (including agentic workflows) and ensure high availability through robust production support, automation, and observability practices.

Qualifications

• 8+ years of work experience
• At least 3+ years with GCP
• Proficient in GCP and related tech stack
• Knowledge of DevOps concepts using GitHub CI/CD
• Experience with production support and GenAI release management
• Familiarity with AI Ops, MCP server, Apigee

Requirements

• Programming & Engineering
  • Python (production-grade development, packaging, testing, performance profiling)
  • Strong understanding of APIs/microservices patterns, containerization, and runtime optimization
• Cloud Technologies (Primary: GCP)
  • Hands-on experience with Google Cloud Platform, including:
    • Vertex AI (pipelines, training, endpoints, model registry, monitoring)
    • Cloud Storage, BigQuery, Pub/Sub
    • Cloud Run and/or GKE
    • Cloud Logging/Monitoring, IAM, Secret Manager, VPC networking
• Data/ML/MLOps Platforms (any of the below)
  • Experience with at least one: Vertex AI / Databricks / Amazon SageMaker / Azure Machine Learning
  • MLOps fundamentals: pipeline orchestration, model registry, lineage, reproducibility, model monitoring, and automated retraining
• GenAI / Agentic Frameworks (Mandatory)
  • Working knowledge of LangChain and LangGraph
  • RAG patterns, prompt and response handling, tool/function calling concepts, evaluation approaches
• Databases / Vector Stores (Any)
  • Experience with databases and/or vector DBs (e.g., pgvector, Pinecone, Weaviate, Milvus, Elastic, OpenSearch, etc.)
  • Understanding of embeddings lifecycle, indexing strategies, and retrieval performance considerations
• DevOps / CI-CD
  • GitHub CI/CD (e.g., GitHub Actions), branching strategies, release workflows

Company Description