Principal Engineer - GenAI, Big Data

Leverify; Pakistan; Hybrid; PKR 2,000,000 - 2,750,000; Full time

Job summary

A leading technology company is seeking a Principal Engineer to lead the design of next-generation intelligent systems. Responsibilities include architecting AI solutions and collaborating closely with data scientists and engineers. The ideal candidate should have over 3 years of experience with ML/LLM solutions, strong programming skills in Python, and a background in distributed systems. This role offers a hybrid work environment based in Islamabad with remote options available.

Qualifications

• 3+ years of experience in building and deploying ML/LLM solutions in production.
• Hands-on expertise with RAG system design.
• Knowledge of indexing algorithms and cloud-native development.

Responsibilities

• Lead design, architecture, and development of intelligent systems.
• Design and deliver production-grade RAG systems.
• Collaborate with data scientists and engineers for model deployment.

Skills

Building and deploying ML/LLM solutions

RAG system design

Indexing algorithms

Agent orchestration frameworks

Distributed systems

Cloud-native development

Programming in Python

Tools

Kafka

Debezium

AWS

Job description

Role

Role - Principal Engineer; Location - Hybrid for Islamabad; Remote for other cities

Role Summary: Lead the design, architecture, and development of next-generation intelligent systems at the intersection of Generative AI, Big Data, and Cloud. Define technical roadmaps, build production-grade multi-agent platforms, and drive innovation across distributed systems, lakehouse architectures, and LLMOps. Operate as a hands-on technical leader and mentor without formal management responsibilities.

Key Responsibilities

• Design and deliver production-grade RAG systems with embedding refresh strategies, vector DB synchronization, and hybrid search.
• Architect and implement AI agent orchestration frameworks (ReAct, multi-agent coordination, persistent state, error recovery, observability).
• Build scalable event-driven architectures with idempotency, exactly-on-once/at-least-once semantics, poison message handling, and backpressure management.
• Contribute to Lakehouse data architectures (Delta Lake, Iceberg, Hudi), addressing schema evolution, compaction, and ACID transactions on object storage.
• Develop high-performance ML/LLM code for real-time pipelines, extending frameworks when required.
• Collaborate with data scientists and platform engineers to accelerate model experimentation, validation, and deployment.
• Define and implement LLMOps strategies including prompt versioning, token cost tracking, evaluation, and personalization.
• Drive architectural vision through design/code reviews, mentorship, and thought leadership.
• Innovate in Generative AI, distributed systems, and intelligent platforms from concept through delivery.

Must-Have Skills & Tools

• 3+ years of building and deploying ML/LLM solutions in production (RAG, LLM fine-tuning, embeddings).
• Hands-on expertise with RAG system design: document chunking, vector DB synchronization, retrieval evaluation.
• Deep knowledge of indexing algorithms (HNSW, IVF, LSH) and hybrid search.
• Proven experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI, or custom).
• Strong background in distributed systems and event-driven architectures (Kafka, Debezium, CDC, DLQs).
• Cloud-native development expertise (AWS).
• Strong programming skills (Python).

Nice-to-Have Skills

• Experience with Graph ML and Graph RAG (ontologies, semantic layers, GNNs).
• Familiarity with Big Data tools (Spark, Flink, PySpark, Glue, Druid).
• Hands-on work with Lakehouse technologies (Delta, Iceberg, Hudi).
• Designing evaluation frameworks for LLMs and multi-agent systems.
• Experience handling unstructured data pipelines (PDFs, tables, images) and real-time personalization.

Soft Skills / Traits

• Strong problem-solving in complex and ambiguous scenarios.
• Excellent collaboration across data, AI, and engineering teams.
• Ability to mentor peers and influence architectural decisions.
• Clear technical communication skills for design reviews and cross-team discussions.