AI/ML Engineer

Join Toptal’s Elite Network of Freelance TalentToptal is an exclusive network of the world’s top freelance software developers, designers, finance experts, product managers, and project managers. As one of the fastest-growing fully remote networks globally, we empower professionals to thrive in their freelance careers while working with leading companies around the world.About the Client:Our client is a global technology research and advisory organization that partners with leading enterprises to drive data-driven decision-making and innovation.Role: AI/ML EngineerWe are looking for several AI and ML Engineering team members who can operate across the full ML platform lifecycle, from building data pipelines and training workflows to deploying, scaling, and monitoring production ML systems. This role blends ML engineering, cloud, and DevOps, MLOps, and API and integration engineering. Depending on seniority, you may also provide technical leadership, set standards, and guide delivery across multiple workstreams.General Information:You will partner with product and engineering stakeholders to design, build, and operate reliable ML-enabled services in cloud environments. The work includes operational automation, event-driven and streaming integrations, and production-grade deployment and monitoring practices. The environment uses AWS heavily and may involve multi-cloud patterns and Azure DevOps for CI/CD.Tasks and Deliverables:Design and implement end-to-end ML workflows including data ingestion, feature generation, training, evaluation, deployment, and monitoringBuild and maintain data pipeline architectures and ETL or ELT workflows using AWS services such as S3, Glue, and KinesisDeploy ML models to production using AWS services such as SageMaker and containerized runtimes on ECS, EKS, and KubernetesImplement model monitoring, observability, alerting, and automated remediation for production ML systemsDevelop backend services and internal tooling in Python, including automation, integrations, and API developmentBuild and maintain APIs using FastAPI or Flask to expose model inference and ML platform capabilitiesImplement event-driven architectures and streaming integrations, with Kafka as a primary technology where applicableCreate and maintain CI/CD pipelines using Azure DevOps and AWS-native tooling to support rapid and reliable releasesApply infrastructure as code and configuration management practices to ensure reproducible environments and secure deploymentsImprove operational automation and reliability across ML platform components, including scaling, cost controls, and incident responseFor lead-level scope: set technical direction, define platform standards, review designs and code, mentor engineers, and drive cross-team executionRequired Experience:AI and ML Engineer scope: 5+ years of experience in cloud engineering, DevOps, or ML engineering with a focus on operational automationLead AI and ML Engineer scope: 7+ years of experience in cloud engineering, DevOps, or ML engineering with a focus on operational automation, including ownership of technical direction for complex systemsHands-on experience with AWS services, including SageMaker, S3, Glue, Kinesis, ECS, and EKSStrong Kubernetes experience, including container orchestration in production and familiarity with multi-cloud environmentsStrong Python programming skills, including automation, integration, and API developmentExperience deploying and monitoring ML models in production environments, including observability and lifecycle managementKnowledge of event-driven architectures and streaming technologies, especially KafkaFamiliarity with CI/CD practices using Azure DevOps and AWS DevOps toolingUnderstanding of data pipeline architectures and experience building ETL or ELT workflowsExperience building APIs with FastAPI or FlaskKnowledge of infrastructure as code and configuration management toolsEngagement Highlights:Opportunity to build and operate production ML systems that prioritize reliability, automation, and scaleWork across ML platform components including data pipelines, streaming, model serving, and observabilityModern cloud and container stack with strong emphasis on Kubernetes and AWS servicesClear runway for impact through operational automation, platform standardization, and measurable improvements to deployment velocity and system stabilityLead-level scope available for candidates who can mentor, set direction, and drive execution across teams and stakeholdersAdditional Details:Location: Remote in IndiaType: 40h/week ContractTimezone: Primarily IST (India Standard Time); Flexible schedule with required overlap for EST meetings; Estimated overlap window: 3-7 PM IST for client meetingsDuration: 6–12 months with strong potential for extensionAuthorization: Applicants must be authorized to work in their country of residence without employer sponsorship

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