AI Engineer

Johar Town, Lahore (Hybrid)

Job Description

About the Role

We're hiring an AI Engineer to help us build intelligent, LLM-powered systems and products. We welcome candidates from diverse backgrounds — whether you come from software engineering, data science, research, or a non-traditional path, what matters most is that you're deeply curious about AI and have hands-on experience shipping things with it. You'll work at the intersection of applied AI research and production engineering, turning the latest advances in large language models into reliable, scalable features our users actually rely on.



Key Responsibilities

• Design and build end-to-end LLM-powered applications, including RAG pipelines, AI agents, and copilot-style features
• Evaluate and select the right foundation models (OpenAI, Anthropic, open-source) for different use cases
• Develop prompt engineering strategies and manage prompt versioning across environments
• Build retrieval systems using vector databases (Pinecone, Weaviate, pgvector, etc.) to ground LLMs in company-specific knowledge
• Instrument and monitor AI systems for accuracy, latency, cost, and safety in production
• Stay current with rapidly evolving GenAI tooling and bring relevant advances into our stack
• Collaborate with product, design, and engineering teams to translate AI capabilities into real product value
  
  

Required Skills & Qualifications

• 2+ years of experience in a technical role — this could be software engineering, data science, ML research, or any background where you've built and shipped real products
• Hands-on experience building production-grade LLM applications
• Strong Python skills and familiarity with LLM frameworks (LangChain, LlamaIndex, or similar)
• Solid understanding of RAG architectures, embedding models, and vector search
• Experience with prompt engineering and LLM evaluation methodologies
• Comfort working with cloud-hosted model APIs as well as open-source models
• A pragmatic mindset — you know when to use a simple prompt and when to build a complex agent
  
  

Nice to Have

• Experience with fine-tuning or RLHF workflows
• Familiarity with AI safety, guardrails, and responsible AI practices
• Background in ML/MLOps (model serving, monitoring, feature stores)
• Contributions to open-source AI projects
  
  

How To Apply

Interested candidates are invited to submit their resume and a cover letter detailing their relevant experience and why they are a good fit for this position. Fill out the form here!.

Datum Labs is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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Key Responsibilities

• Design and build end-to-end LLM-powered applications, including RAG pipelines, AI agents, and copilot-style features
• Evaluate and select the right foundation models (OpenAI, Anthropic, open-source) for different use cases
• Develop prompt engineering strategies and manage prompt versioning across environments
• Build retrieval systems using vector databases (Pinecone, Weaviate, pgvector, etc.) to ground LLMs in company-specific knowledge
• Instrument and monitor AI systems for accuracy, latency, cost, and safety in production
• Stay current with rapidly evolving GenAI tooling and bring relevant advances into our stack
• Collaborate with product, design, and engineering teams to translate AI capabilities into real product value
  
  

Required Skills & Qualifications

• 2+ years of experience in a technical role — this could be software engineering, data science, ML research, or any background where you've built and shipped real products
• Hands-on experience building production-grade LLM applications
• Strong Python skills and familiarity with LLM frameworks (LangChain, LlamaIndex, or similar)
• Solid understanding of RAG architectures, embedding models, and vector search
• Experience with prompt engineering and LLM evaluation methodologies
• Comfort working with cloud-hosted model APIs as well as open-source models
• A pragmatic mindset — you know when to use a simple prompt and when to build a complex agent
  
  

Nice to Have

• Experience with fine-tuning or RLHF workflows
• Familiarity with AI safety, guardrails, and responsible AI practices
• Background in ML/MLOps (model serving, monitoring, feature stores)
• Contributions to open-source AI projects
  
  

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