Senior AI Engineer

HR POD - Hiring Talent Globally

Job Location:

Lahore - Pakistan

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

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.

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 profici...

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.