Machine Learning Engineer

Job Description:

We are seeking a highly skilled and innovative Machine Learning Engineer with hands-on experience in Generative AI technologies to join our growing AI team.
The ideal candidate will design, build, and deploy advanced AI models focused on text, image, audio, and video generation, while working closely with data scientists, researchers, and product teams to create cutting-edge solutions.

Responsibilities:

• Research, design, develop, and deploy Generative AI models (such as LLMs, Diffusion Models, GANs, VAEs, etc.)
• Fine-tune and customize foundation models (e.g., GPT, Llama, Stable Diffusion, DALL-E, etc.) for specific business use cases.
• Develop efficient training pipelines for large-scale machine learning models.
• Collaborate with cross-functional teams (Product, Engineering, Data Science) to integrate AI solutions into products.
• Conduct model evaluations, A/B testing, and performance optimization.
• Stay up to date with the latest advancements in machine learning and generative AI research.
• Write clean, efficient, scalable, and well-documented code.
• Drive experiments, proof of concepts (PoCs), and deployment of AI models into production environments.

Requirements:

• Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or related fields (Ph.D. is a plus).
• 3+ years of industry experience in Machine Learning, with at least 1+ years specifically in Generative AI.
• Proficiency with Python and ML libraries such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, etc.
• Experience in model training, fine-tuning, and evaluation of Large Language Models (LLMs) or other generative models.
• Strong knowledge of NLP, Computer Vision, or Multi-modal learning.
• Familiarity with cloud platforms (AWS, Azure, Google Cloud Platform) and scalable ML deployment frameworks.
• Understanding of prompt engineering, retrieval-augmented generation (RAG), and LLMOps best practices is highly desirable.
• Knowledge of data security, bias mitigation, and model explainability techniques is a plus.

Preferred Skills:

• Experience working with OpenAI, Anthropic, Cohere, or similar API ecosystems.
• Publications or participation in ML research communities (e.g., NeurIPS, ICML, ICLR) is a plus.
• Hands-on experience with vector databases (e.g., FAISS, Pinecone) and knowledge graphs.
• Knowledge of MLOps pipelines, CI/CD, and containerization (Docker/Kubernetes).