Job description
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-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.
