GCP Devops, Terraform/Consultant Specialist

• Manage and monitor core GCP services, ensuring stable day-to-day cloud operations.
• Design and operate scalable, highly available, and secure cloud infrastructure on Google Cloud.
• Build and maintain CI/CD pipelines to enable fast, reliable application releases.
• Automate build/test/deploy/rollback workflows and improve deployment efficiency with engineering teams.
• Implement Infrastructure as Code using Terraform (and Helm where applicable) with version control and repeatable environments.
• Establish monitoring, alerting, and observability; troubleshoot infra and pipeline issues to restore service quickly.
• Drive security-by-design and compliance adherence, partnering with security and documenting architectures/runbooks.
• Develop and optimise RAG solutions: document ingestion, chunking strategies, embedding workflows, vector search, reranking, and citation/traceability.
• Implement evaluation and testing for AI systems: offline test sets, regression suites, adversarial testing (prompt injection/jailbreak), and quality metrics.
• Establish MLOps/LLMOps practices: CI/CD for AI components, versioning (prompts/models/data), automated checks, and controlled releases (canary/rollback).

Required Skills & Experience

• Bachelor’s degree in Computer Science/IT (or equivalent practical experience).
• 5+ years of industry experience, including 3+ years designing and running production systems on GCP. Strong hands-on Kubernetes/GKE and containerization (Docker) experience; microservices understanding.
• Solid IaC skills with Terraform and Helm; able to standardize dev/stage/prod environments. CI/CD expertise with Jenkins (or similar tooling) and DevOps best practices. Scripting proficiency in Python and/or Bash for automation.
• Familiarity with key GCP services (e.g., Compute Engine, GKE, Cloud Storage, BigQuery, IAM) and event streaming (Kafka and/or Pub/Sub); data platform experience on GCP is a plus.
• Strong software engineering fundamentals: system design, API design, distributed systems, and clean coding practices. Hands-on experience delivering production-grade AI solutions, including at least one of:
• LLM integrations (prompt orchestration, tool/function calling, structured outputs), or ML model deployment/inference services, or Search/RAG and knowledge retrieval platforms. Proficiency in Python plus at least one backend language (e.g., Java, Go, C#, TypeScript).
• Experience with modern deployment practices: containers (Docker), CI/CD, and cloud-native services. Experience with vector search technologies (e.g., OpenSearch/Elastic, pgvector, FAISS, Pinecone) and embedding/reranking techniques. Familiarity with LLM frameworks/patterns (e.g., LangChain, LlamaIndex) and prompt versioning/evaluation tooling.
  • Knowledge of performance optimization techniques: caching, batching, rate limiting, quantisation, latency/cost tuning. Background in regulated environments with strong governance and auditability requirements.