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 .