Key Responsibilities

Model Development

• Design, build, and optimize AI models across multiple domains, including:NLP (text understanding, chatbots, document processing)
• LLM-driven applications (RAG, agents)
• Computer Vision (object detection, OCR, video analytics)
• Fine-tune and customize pre-trained models for specific domain requirements
• Explore and evaluate new architectures and techniques to enhance model performance

Data Management

• Gather, preprocess, and curate datasets for NLP and Computer Vision projects
• Apply data augmentation strategies for text and image data
• Maintain high data quality and relevance to support effective model training and evaluation

Deployment & Integration

• Deploy and integrate AI models into production environments
• Monitor model performance and implement continuous improvement strategies
• Optimize deployment pipelines for efficiency and scalability

Monitoring & Optimization

• Monitor model performance and identify areas for improvement
• Implement monitoring and alerting systems to detect performance issues
• Optimize model inference pipelines for latency and cost efficiency

Collaboration

• Partner with product, engineering, and business teams to deliver AI-powered solutions
• Contribute to system design and architectural decisions
• Promote knowledge sharing and best practices within the team

Continuous Innovation

• Keep abreast of the latest developments in LLMs, NLP, and Computer Vision
• Assess, adopt, and integrate new tools, frameworks, and methodologies to drive innovation

Requirements

• Bachelor's degree in Computer Science, AI, Machine Learning, or a related field, or equivalent practical experience
• At least 2 years of experience in AI/ML engineering, with a focus on NLP, LLMs, or Computer Vision
• Proven hands-on experience designing, building, and deploying real-world AI systems
• Experience with multimodal AI (text and image)
• Familiarity with vector databases such as FAISS, Milvus, or Pinecone
• Experience with real-time inference systems
• Basic understanding of Docker and Kubernetes