In this high-impact role, you will architect and deploy sophisticated AI solutions that bridge the gap between complex data science research and scalable production environments.

You will collaborate closely with cross-functional product teams, data scientists, and senior stakeholders to translate ambiguous business challenges into robust, AI-driven products.

Your work will directly influence our core product roadmap, enabling us to deliver hyper-personalized experiences and intelligent automation that drive significant business growth and operational efficiency for our global client base.

Key Responsibilities :

- Design and implement end-to-end machine learning pipelines on AWS to ensure high availability and performance of AI models in production environments.

- Lead the development of advanced NLP and Generative AI applications, including fine-tuning and deploying Large Language Models (LLMs) to solve complex enterprise-level problems.

- Mentor junior engineering talent and conduct rigorous code reviews to maintain high standards of technical excellence across the AI/ML department.

- Collaborate with product managers to define technical requirements and translate business objectives into actionable AI strategies that improve user engagement and system accuracy.

- Optimize deep learning architectures to reduce latency and infrastructure costs, ensuring that our AI solutions remain both scalable and cost-effective.

Required Skillset :

- Demonstrated expertise in building and deploying production-grade machine learning models, with a deep understanding of Deep Learning frameworks and NLP techniques.

- Proven ability to architect scalable AI infrastructure on AWS, utilizing cloud-native tools to manage data pipelines and model deployment lifecycles.

- Strong proficiency in implementing and fine-tuning Generative AI and LLM-based solutions to address specific business use cases.

- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders and influence cross-functional decision-making.

- A collaborative mindset with the ability to thrive in a hybrid work environment, balancing independent research with team-based development cycles.

- A Master's or Bachelor's degree in Computer Science, Mathematics, or a related quantitative field from a premier institute, supported by 8 to 10 years of hands-on experience in the AI/ML domain.