Jobs → Engineering → AI/ML Engineer

About the Role We are seeking an experienced AI/ML Engineer to design and implement end-to-end AI solutions within AWS cloud environments. Key Responsibilities • Design and architect end-to-end AI, machine learning, and generative AI solutions within AWS cloud environments • Build and deploy real-time and batch inference applications using services such as SageMaker, ECS, API Gateway, and related cloud technologies • Develop enterprise-grade generative AI and agentic AI applications leveraging Gemini, Vertex AI, LLMs, RAG architectures, and orchestration frameworks • Design and implement scalable feature store solutions supporting both real-time and batch machine learning workflows • Build and maintain CI/CD pipelines, infrastructure-as-code deployments, model serving frameworks, and automated operational processes • Develop integration patterns connecting AI applications to enterprise systems, APIs, data platforms, and business workflows • Implement monitoring, observability, model performance tracking, and SLA management for production AI systems • Optimize AI applications for scalability, reliability, latency, cost, and overall production performance • Collaborate with Data Scientists to operationalize machine learning models and accelerate production deployment • Provide technical leadership on architecture decisions, cloud service selection, and AI engineering best practices Requirements • 10+ years of experience in AI/ML engineering • Expertise in AWS AI Platform Architecture including SageMaker, ECS, API Gateway, Real-Time Inference, and Infrastructure Design • Strong background in Generative AI Engineering with Gemini, Vertex AI, Agentic AI, RAG, and LLM Application Development • Proficiency in MLOps and Feature Platform Engineering including Feature Stores, Streaming Pipelines, CI/CD, Model Monitoring, and Observability • Experience with real-time and batch inference applications • Knowledge of infrastructure-as-code deployments and automated operational processes • Understanding of integration patterns and enterprise systems • Experience with monitoring, observability, and SLA management • Ability to optimize AI applications for scalability, reliability, latency, and cost • Leadership experience in architecture decisions and AI engineering best practices