Senior Cloud AI Engineer (Hybrid, Lahore, USD Salary)

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.