Senior AI/ML Engineer

WAMO LABS; Lahore; Hybrid; PKR 300,000 - 400,000; Full time

Benefits offered by this job

Competitive salary

Bi-annual performance bonuses

Generous paid time off

Annual learning budget

Hardware & tools stipend

Home internet stipend

Health & wellness coverage

Hybrid & remote flexibility

Quarterly team events & annual retreat

Job summary

WAMO LABS is seeking an AI/ML Engineer to advance LLM fine-tuning, data pipelines, model optimization, and production deployments. You will build high-performance AI systems, experiment with new models, and contribute to advanced ML initiatives.

You will tune LLMs, optimize inference, and deploy production-ready endpoints while collaborating with product and engineering teams to deliver robust ML features and maintainability.

Qualifications

• 2–4 years of industry experience in AI/ML or NLP.
• Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow).
• Hands-on experience with LLM fine-tuning techniques (LoRA, PEFT, QLoRA).
• Understanding of embeddings, transformers, and inference optimization.
• Experience building ETL pipelines and working with structured/unstructured data.
• Familiarity with vector databases (Pinecone, Weaviate, Milvus, Chroma).
• Experience with FastAPI and production deployments.
• Strong analytical skills, experimentation mindset, and curiosity.
• Good communication skills for cross-team collaboration.

Responsibilities

• Perform LLM fine-tuning (LoRA, SFT), embeddings training, and model evaluation.
• Optimize inference latency, throughput, and memory usage for deployed models.
• Build and maintain ETL pipelines for model training and data preparation.
• Integrate vector databases and design RAG pipelines for production use cases.
• Deploy models using FastAPI and maintain production endpoints.
• Work on voice model integrations (STT, TTS, RVC, etc.) as needed.
• Build ML experiments, maintain versioning, logging, and reproducibility.
• Collaborate closely with product and engineering teams to define ML features.
• Communicate clearly about model limitations, performance, and expected outcomes.

Job description

We are looking for an AI/ML Engineer to work on LLM fine-tuning, intelligent data pipelines, model optimization, and production-grade deployments. You will help build high-performance AI systems, experiment with new models, and contribute to advanced ML/LLM initiatives.

What you'll do

• Perform LLM fine-tuning (LoRA, SFT), embeddings training, and model evaluation.
• Optimize inference latency, throughput, and memory usage for deployed models.
• Build and maintain ETL pipelines for model training and data preparation.
• Integrate vector databases and design RAG pipelines for production use cases.
• Deploy models using FastAPI and maintain production endpoints.
• Work on voice model integrations (STT, TTS, RVC, etc.) as needed.
• Build ML experiments, maintain versioning, logging, and reproducibility.
• Collaborate closely with product and engineering teams to define ML features.
• Communicate clearly about model limitations, performance, and expected outcomes.

What we're looking for

• 2-4 years of industry experience in AI/ML, NLP, or applied ML engineering.
• Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow).
• Hands-on experience with LLM fine-tuning techniques (LoRA, PEFT, QLoRA, etc.).
• Understanding of embeddings, transformers, and inference optimization.
• Experience building ETL pipelines and working with structured/unstructured data.
• Familiarity with vector DBs (Pinecone, Weaviate, Milvus, Chroma).
• Experience with FastAPI and production deployments.
• Strong analytical skills, experimentation mindset, and curiosity.
• Good communication skills for cross-team collaboration.

Competitive up to 400k

• Performance bonuses paid bi-annually
• Generous paid time off, planned and casual
• Annual learning budget for courses, certifications, and conferences
• Hardware & tools stipend on day one
• Home internet stipend, monthly
• Health & wellness coverage
• Hybrid & remote flexibility where the work allows
• Quarterly team events and an annual retreat