Staff Machine Learning Engineer
Required skills
Strong backend development experience in Python (preferred) or Java. Experience building API-driven and event-driven services for AI inference, agent orchestration, tool invocation, and data pipelines. Hands-on experience with Generative AI and Large Language Models (LLMs). Experience with agentic AI systems and agent orchestration frameworks such as LangGraph. Strong experience with Kubernetes and containers, including deploying and scaling ML training and inference workloads across CPU and GPU environments. Solid background in DevOps/MLOps and CI/CD, including automated training, evaluation, and model promotion pipelines. Experience with source control and CI/CD tools such as Git, Jenkins, ArgoCD, or similar. Hands-on experience with AWS, GCP, or Azure. Experience designing and operating high-performance AI/ML systems.
Preferred skills
Proven technical leadership, with experience influencing system architecture and mentoring engineers on ML platforms and cloud-native best practices. Strong systems-thinking ability to balance model quality, scalability, reliability, latency, and cost in real-world AI deployments. Demonstrated ability to identify and drive adoption of new AI/ML approaches and translate them into scalable production solutions. Deep understanding of AI/ML systems and their integration into cloud-native environments.
Technologies
Python, Java, API-driven services, event-driven services, Generative AI, Large Language Models (LLMs), agentic AI systems, agent orchestration frameworks, Kubernetes, containers, DevOps/MLOps, CI/CD, source control, Git, Jenkins, ArgoCD, AWS, GCP, Azure, high-performance AI/ML systems.
Responsibilities
Partner with engineering, product, and data science teams to convert requirements into prototypes and production-ready AI systems. Build, deploy, and scale AI/ML and Generative AI services, taking solutions from experimentation through full production. Design and operate high-performance AI systems, optimizing for inference latency, throughput, and cost efficiency at scale. Drive adoption of agentic AI architectures, LLM-powered features, and modern ML platform capabilities. Proactively identify innovation opportunities, explore emerging AI/ML approaches, and turn them into concrete technical proposals. Stay current with AI/ML advancements and apply relevant innovations to active initiatives. Influence system architecture and guide engineering best practices for ML platforms and cloud-native AI systems.
Seniority
Staff
Domain
AI/ML, Generative AI, cloud-native systems, ML platforms, agentic AI, DevOps/MLOps, CI/CD, Kubernetes, containers, AI inference, data pipelines, LLMs, AI systems, cloud computing.