What You'll Do:

Build & Scale the AI Platform Foundation

• Design, build, and maintain the ML platform from the ground up, including experiment tracking, model registries, model serving, CI/CD pipelines, and monitoring capabilities.
• Establish scalable infrastructure standards and engineering best practices that enable rapid AI solution development and deployment.
• Contribute to the creation of reusable AI platform components that support multiple use cases across the organization.
• Drive platform reliability, observability, security, and scalability from design through production.

Develop AI & GenAI Infrastructure

• Own and enhance the infrastructure supporting GenAI applications, including vector databases, LLM serving frameworks, retrieval systems, and evaluation frameworks.
• Design and implement scalable Retrieval-Augmented Generation (RAG) architectures and supporting infrastructure.
• Build and maintain evaluation, monitoring, and observability frameworks for AI and GenAI systems.
• Support the deployment and operationalization of both classical machine learning models and GenAI applications.

Build Data Pipelines & Production Integrations

• Design and implement robust data pipelines that power machine learning and GenAI solutions in production environments.
• Develop integration layers that reliably connect AI services and outputs with existing enterprise systems and business applications.
• Build APIs, services, and distributed system components that support scalable AI product delivery.
• Collaborate closely with AI engineers, software engineers, product teams, and business stakeholders to ensure seamless adoption of AI solutions.

Enable AI Use Case Delivery

• Partner with cross-functional teams to accelerate the deployment of AI use cases into production.
• Ensure AI solutions meet requirements for reliability, performance, security, and maintainability.
• Support the evaluation and adoption of emerging AI technologies, tools, and frameworks.
• Contribute to the continuous improvement of the AI development ecosystem.

What It Takes:

Technical

• 3+ years of experience in ML Platform Engineering, AI Infrastructure Engineering, MLOps, Software Engineering, or related fields.
• Hands-on experience building and maintaining ML platforms, including experiment tracking, model registries, model serving, and ML CI/CD pipelines.
• Strong experience with ML infrastructure tools such as MLflow, Weights & Biases (W&B), or similar platforms.
• Experience building and supporting GenAI infrastructure, including vector databases (Pinecone, Weaviate, pgvector), LLM serving frameworks, and RAG architectures.
• Knowledge of AI evaluation and observability tools such as RAGAS, LangSmith, or equivalent solutions.
• Experience working with cloud-native AI and ML services, preferably AWS SageMaker.
• Strong software engineering skills, including API development, distributed systems design, and backend application development.
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Experience integrating AI systems with enterprise applications through REST APIs, message queues, and service-based architectures.
• Understanding of infrastructure automation, monitoring, scalability, and production-grade system design.

Collaboration & Problem Solving

• Ability to work effectively within Agile teams and cross-functional environments.
• Strong analytical and systematic problem-solving capabilities.
• Ability to balance technical excellence with practical business outcomes.
• Effective communication skills and the ability to collaborate across global teams and functions.
• Comfortable operating in fast-paced environments where processes and platforms are still evolving.

Mindset

• Builder mentality with a passion for creating scalable platforms and systems from the ground up.
• Strong ownership mindset with a focus on delivering reliable and maintainable solutions.
• Curiosity to explore emerging AI technologies while maintaining engineering rigor.
• Systems-thinking approach that prioritizes scalability, reliability, observability, and long-term maintainability.
• Passion for enabling others by creating platforms and tools that accelerate innovation.