Senior AI Engineer (Onsite, Lahore, Remittance Salary)

Requirements:

• PhD in Computer Science, Machine Learning, NLP, AI, Data Science, or a closely related field.
• Demonstrated applied research background with publications, patents, or real-world AI deployments.
• 4 – 8 years of experience in AI/ML engineering or related fields.
• Expert-level Python skills and proficiency in modern ML/AI frameworks.
• Strong expertise in large language models (LLMs), embeddings, and vector search technologies.
• Hands-on experience with agentic (AG) architectures and LLM orchestration frameworks such as LangChain, LlamaIndex, and custom-built stacks.
• Proficiency in prompt engineering, including prompt design, optimization, and evaluation methodologies.
• Solid understanding of distributed systems, microservices architecture, and cloud-native implementations.
• Production experience deploying AI systems in enterprise or regulated environments.

Responsibilities:

• Define the end-to-end AI roadmap and steer long-term technical decisions.
• Drive model architecture, experimentation, and evaluation strategies based on current research trends.
• Guide the transition from research prototypes to production-hardened enterprise AI systems.
• Introduce methodologies to ensure reliability, trustworthiness, and explainability in AI systems.
• Architect and optimize LLM-based solutions, including RAG pipelines, agentic workflows, and custom tooling.
• Evaluate, fine-tune, and integrate state-of-the-art models (open-source and proprietary).
• Lead advanced experimentation: model distillation, hybrid search strategies, prompt optimization, hallucination detection, etc.
• Build frameworks for automatic evaluation, versioning, and safe deployment of large-scale AI systems.
• Address enterprise GenAI challenges related to data security, governance, and privacy.
• Reduce hallucinations, improve factual grounding, and optimize latency and inference performance.
• Build scalable, highly available LLM serving architectures with efficient multi-model routing, cost control, and strong observability.
• Work closely with product teams to shape features and AI-driven value propositions.
• Assess enterprise workflows and identify AI-led automation or augmentation opportunities.
• Communicate complex AI concepts to stakeholders in clear, business-oriented language.
• Mentor engineers and establish internal AI engineering standards and best practices.