Responsibilities
- Train, fine-tune, assess, and deploy deep learning models, including large language and generative models
- Construct reliable, reusable machine learning pipelines and APIs using Python and FastAPI, integrated into scalable backend environments
- Help develop cloud-native AI services using AWS technologies such as Lambda, SageMaker, S3, and API Gateway
- Design and maintain MLOps workflows to ensure model reproducibility, automation, and monitoring
- Work with backend engineers to integrate models into production via APIs and event-driven architectures
- Produce clean, well-documented, and maintainable code for training and deployment pipelines
- Monitor deployed models for performance issues, data drift, and implement feedback mechanisms
- Contribute to prompt engineering, model assessment, and optimization of generative models in production
- Proactively communicate progress, challenges, and technical solutions during planning and discussions
- Review and analyze recent research in LLMs, generative models, and deep learning; implement promising techniques
- Lead experimentation and benchmarking across modeling approaches such as transformers, diffusion models, RAG, LoRA, and quantization
- Help select tools and frameworks aligned with the organization’s AI/ML technical direction
- Support strategic AI efforts focused on personalization, automation, and intelligent platform enhancements
- Promote best practices in model reproducibility, explainability, and performance tracking
- Stay current with advancements in AI, suggesting innovations and system improvements
- Communicate clearly with cross-functional teams including product, backend, frontend, and DevOps
- Provide meaningful input in technical discussions on model design, performance, and trade-offs
- Share experimental results, research insights, and technical findings through documentation or internal presentations
- Collaborate respectfully with peers to build innovative and sustainable AI features
- Act as a reliable, constructive team member with a collaborative and growth-focused mindset
- Engage in cross-team efforts with designers, engineers, and product stakeholders to align AI development with user needs
- Participate in team meetings, planning, and brainstorming sessions
- Support a positive, inclusive, and transparent engineering environment
- Demonstrate curiosity, humility, and continuous learning in daily work
- Foster a culture of experimentation and ongoing improvement in AI development
