Senior AI/ML Engineer

Experience: Minimum 5 years

Employment Type: Full-time

About the Role:

We are looking for an experienced Principal Machine Learning Engineer to architect and implement advanced multimodal RAG (Retrieval-Augmented Generation) pipelines that integrate vision and language understanding. The ideal candidate will bring deep expertise in Machine Learning, Computer Vision, and Natural Language Processing (NLP), with hands-on experience in building scalable AI systems and deploying them using AWS AI services.

Key Responsibilities:

• Architect and implement end-to-end multimodal RAG pipelines, integrating text, image, and other data modalities.
• Apply MLOps best practices for model deployment, monitoring, and continuous improvement using AWS AI services.
• Design and optimize vector databases and embedding retrieval mechanisms for large-scale systems.
• Lead fine-tuning and optimization of Large Language Models (LLMs) using advanced techniques like LoRA, QLoRA, and RLHF.
• Collaborate with cross-functional teams to integrate ML systems into production environments.
• Research and prototype new vision-language models and multimodal architectures.
• Drive scalable deployment strategies for open-source LLMs and manage continuous performance optimization.
• Mentor junior engineers and contribute to the team’s overall technical direction.

Required Skills & Qualifications:

• 5+ years of experience in Machine Learning, Computer Vision, NLP, or related fields.
• Strong understanding of multimodal learning and vision-language models.
• Proven experience building and deploying RAG systems and managing vector databases.
• In-depth knowledge of PyTorch, Transformers, and modern ML frameworks.
• Proficiency in NLP, LLM fine-tuning, and model optimization.
• Hands-on experience with AWS AI/ML services, including model deployment and lifecycle management.
• Familiarity with open-source LLM architectures (e.g., LLaMA, Falcon, Mistral, etc.).
• Excellent problem-solving, communication, and leadership skills.

Preferred Qualifications:

• Experience with multi-cloud or hybrid AI infrastructure.
• Contributions to open-source ML or LLM frameworks.
• Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, or related disciplines.