Role Overview:

As an AI/ML Engineer, you will be responsible for designing, developing, and deploying sophisticated machine learning models that drive business value. You will work closely with cross-functional teams, including data scientists, software engineers, and product stakeholders, to translate business requirements into scalable AI solutions. Your day-to-day will involve building end-to-end pipelines, optimizing model performance, and ensuring seamless integration within cloud-native environments. This role is pivotal in enhancing our product intelligence and ensuring that our AI infrastructure remains resilient, efficient, and capable of handling large-scale data processing tasks.

Key Responsibilities:

- Design and implement end-to-end machine learning pipelines to automate model training, evaluation, and deployment processes for internal and client-facing applications.

- Architect and maintain scalable AI infrastructure on AWS, utilizing SageMaker for model lifecycle management to ensure high availability and performance.

- Develop high-performance APIs using FastAPI and Flask to serve machine learning models, ensuring low-latency communication with downstream services.

- Orchestrate containerized applications using Docker and Kubernetes to ensure consistent deployment environments across development, staging, and production.

- Integrate real-time data streaming solutions using Kafka to facilitate high-throughput data ingestion and feature engineering for predictive modeling.

- Manage infrastructure as code using CloudFormation to ensure reproducible and secure cloud environments that meet enterprise standards.

Required Skillset:

- Demonstrated proficiency in building and deploying machine learning models using Python, with a deep understanding of data structures, algorithms, and software engineering best practices.

- Proven ability to manage the full ML lifecycle on AWS, specifically leveraging AWS SageMaker for training, tuning, and hosting models at scale.

- Strong expertise in containerization and orchestration technologies, including Docker and Kubernetes, to manage complex microservices architectures.

- Experience in designing event-driven architectures using Kafka to handle large-scale data streams effectively.

- Ability to develop robust backend services using FastAPI and Flask, ensuring clean, maintainable, and well-documented code.

- Proficiency in automating cloud infrastructure deployments using CloudFormation to maintain consistent and scalable environments.

- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders and collaborate effectively in a distributed, remote-first environment.

- Candidates must possess 5 - 7 years of relevant experience in the AI/ML domain, demonstrating a track record of delivering production-grade solutions.