Machine Learning Engineer - Generative AI

Role Overview :

As a Machine Learning Engineer specializing in Generative AI and LLMs, you will be at the forefront of building next-generation intelligent systems that redefine how our business interacts with data. You will work closely with cross-functional teams of data scientists, product managers, and software engineers to design, train, and deploy sophisticated language models that solve complex real-world problems. Your day-to-day will involve architecting scalable pipelines, fine-tuning models for specific enterprise use cases, and ensuring that our AI solutions are both performant and ethically sound. By bridging the gap between cutting-edge research and production-grade applications, you will directly influence our product roadmap and deliver transformative value to our global customer base.

Key Responsibilities :

- Architect and implement end-to-end Generative AI solutions using LLMs to automate workflows and enhance user engagement across our digital platforms.

- Develop and optimize RAG (Retrieval-Augmented Generation) pipelines to ensure our models provide accurate, context-aware, and domain-specific responses.

- Build and maintain robust LLMOps infrastructure to streamline the deployment, monitoring, and versioning of models in a production environment.

- Collaborate with stakeholders to translate ambiguous business requirements into technical specifications, ensuring AI initiatives align with long-term strategic goals.

- Conduct rigorous performance evaluation and fine-tuning of models using PyTorch to improve accuracy, latency, and cost-efficiency for high-scale applications.

Required Skillset :

- Demonstrated expertise in building production-grade machine learning systems using Python, with a deep understanding of modern deep learning frameworks like PyTorch.

- Proven ability to design and deploy LLM-based applications using LangChain, ensuring seamless integration with existing enterprise software stacks.

- Strong proficiency in cloud-native development on AWS, specifically leveraging services for model training, hosting, and data orchestration.

- Exceptional problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders, fostering a collaborative team environment.

- A proactive mindset toward continuous learning, with the ability to thrive in a remote-first or distributed work setup while maintaining high standards of code quality and documentation.

- A solid academic foundation in Computer Science, Mathematics, or a related quantitative field, complemented by 5 - 10 years of hands-on experience in machine learning engineering.