AI Engineer - Agentic Data Platform (Onsite, Lahore, USD Salary)

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.