Senior Cloud AI Engineer

2 weeks ago

Lahore, Punjab, Pakistan HR POD - Hiring Talent Globally Full time 800,000 - 1,600,000 per year

Requirements:

  • Bachelor's or Masters degree in Computer Science, Engineering, or a related field.
  • 7+ years of professional software development experience, with at least 3-4 years focused on cloud architecture and backend systems.
  • Proficient in Google Cloud Platform (GCP) with hands-on experience in Compute (Cloud Run, Cloud Functions, GKE), Messaging (Pub/Sub), Storage & Databases (BigQuery, Cloud Storage, Spanner, Firestore), AI/ML tools (Vertex AI, Generative AI APIs), and Networking & Security.
  • Proven experience designing and building event-driven architectures and microservices.
  • Strong programming skills, preferably in Python (especially for AI/ML and GCP), Java, or Go.
  • Experience with workflow orchestration tools (e.g., Apache Airflow, Cloud Workflows, or custom solutions).
  • Solid understanding of AI/ML concepts, including experience with LLMs, RAG, vector databases, and building agentic or autonomous systems.
  • Demonstrable experience implementing resilience patterns (retries, circuit breakers, idempotency) and ensuring system observability.
  • Experience with data modeling, SQL, and NoSQL databases.
  • Proficiency with CI/CD tools, containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform).
  • Excellent problem-solving skills and the ability to architect solutions for complex, ambiguous problems.
  • Experience with building cloud-based AI, Compute, and Storage tools and concepts.
  • Familiarity with knowledge graphs (e.g., Neo4j, JanusGraph) and their application in RAG or knowledge management.
  • Experience with Document AI or similar document processing technologies.
  • Understanding of front-end interactions with backend agentic systems.

Responsibilities:

  • Design AI-driven systems on GCP using agentic pipelines, Pub/Sub workflows, Cloud Run, BigQuery, and automation.
  • Lead backend development of core services, atomic actions, orchestration, and agentic control loops, including context, session, and history management.
  • Integrate AI/ML models (LLMs, custom agents) with focus on RAG, embeddings, and prompt engineering.
  • Implement resilience practices: retries, circuit breakers, backoff, mitigation patterns, and observability (logging, monitoring, watchdogs).
  • Manage data persistence and flow, using tools like BigQuery for immutable record-keeping.
  • Build complex workflows with validation, consensus mechanisms, and automated cleanup actions.
  • Collaborate with PMs, analysts, and engineers; translate requirements; mentor and promote best practices.
  • Advocate CI/CD, infrastructure as code, and automated testing for reliable deployments.
  • Enforce security best practices and ensure compliance.

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