IC1 - ML Platform & Full stack AI Engineer

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.