Role Overview :

We are seeking a seasoned AI Platform Engineer to lead the evolution of our machine learning infrastructure in Bangalore. In this role, you will architect and maintain scalable MLOps and LLMOps pipelines that bridge the gap between experimental data science and production-grade AI services.

You will collaborate closely with cross-functional data science teams, software engineers, and product stakeholders to ensure our AI models are deployed reliably, monitored for performance, and secured against evolving threats. By building robust orchestration frameworks and observability platforms, you will directly influence the speed at which we deliver intelligent features to our customers, ensuring high availability and operational excellence across our cloud ecosystem.

Key Responsibilities :

- Design and implement end-to-end MLOps and LLMOps workflows to accelerate the deployment of generative AI and predictive models into production environments.

- Orchestrate complex data and model pipelines using Apache Airflow to ensure seamless data flow and reliable model retraining cycles for business-critical applications.

- Manage containerized environments using Docker and Kubernetes to provide consistent, scalable infrastructure for diverse AI workloads.

- Establish comprehensive AI observability frameworks to proactively monitor model drift, latency, and performance metrics, ensuring optimal user experiences.

- Implement rigorous security protocols across the AI platform to protect sensitive data and ensure compliance with enterprise governance standards.

- Optimize cloud resource utilization on AWS by refining orchestration strategies, directly impacting the cost-efficiency and performance of our AI initiatives.

Technical Stack & Requirements :

- Experience : 5 - 10 years in Platform Engineering/DevOps, with 3+ years supporting AI/ML or heavy data-engineering workloads.

- Containerization : Expert-level mastery for managing distributed workloads.

- AI/ML & Data Systems : Hands-on familiarity with MLOps frameworks, vector databases, and data platforms.

- Domain Plus : Prior experience in HealthTech, InsurTech, or FinTech is a strong advantage.

Required Skillset :

- Demonstrated expertise in building and scaling production AI platforms, with a deep understanding of the full lifecycle of machine learning and large language models.

- Advanced proficiency in cloud orchestration and container management, specifically leveraging AWS, Kubernetes, and Docker to maintain high-availability systems.

- Strong technical background in data engineering and workflow automation, with the ability to design resilient pipelines using tools like Apache Airflow.

- Proven ability to communicate complex technical architectures to non-technical stakeholders, fostering a culture of collaboration across engineering and product teams.

- A proactive mindset toward security and observability, with the capability to integrate monitoring tools that provide actionable insights into AI model health.

- A Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field from Tier 1 Institute is preferrable, along with 5 to 10 years of hands-on experience in platform or Enterprise AI engineering.