Machine Learning Engineer

We are looking for a Machine Learning Engineer who is comfortable with LLM fine-tuning, prompt engineering, AI model selection and scalable AI deployment. The ideal candidate will be responsible for evaluating, selecting, and optimizing models for domain-specific tasks.   
 

Key Responsibilities 
• Fine-tune LLMs and optimize models for domain-specific applications. 
• Compare and evaluate open-source vs. closed-source models based on task performance, accuracy, latency, cost, and licensing constraints. 
• Conduct benchmarking using perplexity scores, F1-score, BLEU, ROUGE, and latency tests. 
• Implement parameter-efficient fine-tuning (LoRA, Adapters, Quantization) to improve model efficiency. 
• Develop MLOps pipelines using Kubernetes, Ray, MLflow, and Weights & Biases for scalable deployment. 
• Implement model quantization and pruning to optimize for cost and efficiency. 
• Develop prompt engineering strategies and retrieval-augmented generation (RAG) systems. 
• Deploy models using Docker, Kubernetes, and cloud-based solutions. 
• Collaborate with software engineers and data teams to integrate ML models into production. 
•  Collaborate with data engineers to streamline data pipelines for training and inference. 

Qualifications 
•  5+ years of experience in machine learning.  
•  Experience in comparing, selecting, and evaluating open-source vs. closed-source AI models. 
•  Hands-on experience with LLM fine-tuning, transfer learning, and transformers. 
•  Proficiency in Python, PyTorch, Hugging Face, LangChain. 
•  Experience with distributed training techniques  
•  Familiarity with vector databases (FAISS, Pinecone, Weaviate) for LLM applications. 
•  Experience with MLOps best practices (CI/CD, monitoring, logging). 
•  Understanding of model benchmarking and evaluation techniques.

🕒 Type: 3 months contract with long term extension .