AI Engineer

5 days ago

Lahore, Punjab, Pakistan HR POD - Hiring Talent Globally Full time $120,000 - $180,000 per year

Requirements:

  • Bachelors or Masters degree in Computer Science, Software Engineering, AI/ML, Data Science, or related field (or equivalent experience).
  • 5-8 years of professional experience in AI/ML engineering, Data Engineering, or Applied AI development.
  • Solid foundation in Python with experience building scalable AI/ML applications.
  • Hands-on experience with agentic toolkits, including Google Agentic/Vertex AI Agent Builder and the Aera Agentic Platform (or similar autonomous decisioning platforms).
  • Practical exposure to open-source agentic frameworks, such as LangChain, AutoGen, CrewAI, and the HuggingFace stack.
  • Experience deploying LLMs, RAG pipelines, vector databases (e.g., Pinecone, Weaviate, Chroma, BigQuery Vector search).
  • Good understanding of MLOps and LLMOps: CI/CD, model versioning, experiment tracking, and monitoring.
  • Familiarity with cloud ecosystems (GCP preferred; AWS/Azure as a plus).
  • Strong problem-solving and system design skills with ability to work in fast-paced environments.
  • Experience building autonomous decisioning platforms for manufacturing, finance, supply chain, or enterprise automation.
  • Exposure to streaming data systems (Kafka, Kinesis, Pub/Sub).
  • Knowledge of Graph-based reasoning and enterprise knowledge management.
  • Experience working with API-driven enterprise platforms (SAP, Salesforce, ServiceNow, etc.).
  • Excellent communication and documentation abilities.
  • Strong ownership mindset and ability to work independently with minimal supervision.
  • Ability to collaborate with cross-functional technical and business teams.

Responsibilities:

  • Design, architect, and develop agentic AI pipelines and multi-agent systems for data ingestion, processing, and analytics.
  • Build and optimize Lakehouse-based data solutions on Databricks including ETL/ELT pipelines, Delta Lake storage, and ML model operationalization.
  • Implement and orchestrate AI Agents using tools such as Google Vertex AI Agent Builder, Aera/AREA agents, or equivalent agentic frameworks.
  • Integrate LLMs and foundation models into data workflows for autonomous decisioning and self-service insights.
  • Develop, test, and deploy RAG (Retrieval Augmented Generation) and LLMOps workflows to support domain-specific knowledge reasoning.
  • Evaluate and implement best practices for prompt engineering, fine-tuning, guardrails, and responsible AI in production.
  • Collaborate with data engineers, MLOps, and product teams to ensure scalability, resilience, and security of the agentic platform.
  • Monitoring and performance optimization of deployed agents and AI workflows.

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