As an AI / ML Engineer, a typical day involves designing and building advanced applications and systems that leverage artificial intelligence technologies and cloud-based AI services. The role requires integrating generative AI models into solutions, ensuring that applications are production-ready whether deployed on cloud platforms or on-premises. Daily activities may include working on deep learning frameworks, developing neural network architectures, creating intelligent chatbots, and implementing image processing techniques. Collaboration with cross-functional teams to align AI capabilities with business needs and maintaining high-quality pipelines for AI solutions are also key aspects of the role. Roles & Responsibilities: Training data pipeline: format conversion, deduplication, quality filtering Supervised fine-tuning (SFT) with progressive model scaling Offline evaluation framework design Shadow-mode and staged A/B rollout with automated rollback Expected to be an SME, collaborate and manage the team to perform.
Responsible for team decisions.
Engage with multiple teams and contribute on key decisions.
Provide solutions to problems for their immediate team and across multiple teams.
Lead the design and implementation of AI and machine learning models to meet project requirements.
Mentor junior team members and support their professional growth within the AI and machine learning domain.
Coordinate with stakeholders to ensure alignment of AI initiatives with organizational goals.
Professional & Technical Skills:
Must To Have Skills: Proficiency in Machine Learning (ML), Large Language Models (LLMs), Python (Programming Language), AWS AI Services.
Experience with Large Language Models (LLMs), Python (Programming Language), AWS AI Services.
5+ years hands-on LLM fine-tuning (SFT/DPO) ML serving infrastructure on AWS GPU instances Production monitoring with canary deployments Python, Hugging Face ecosystem, model quantization, end-to-end MLOps CI/CD
Strong knowledge of deep learning techniques including neural networks and their practical applications.
Experience in developing and deploying AI models in cloud environments and on-premises infrastructure.
Familiarity with generative AI models and their integration into production systems.
Ability to optimize machine learning pipelines for scalability and performance.
Additional Information:
The candidate should have minimum 7.5 years of experience in Machine Learning (ML).
