Head of AI/ML Engineering

Role DescriptionThe Head of AI/ML Engineering will lead the development of internal AI platforms, delivery of client-facing AI solutions, and creation of scalable AI products for industrial use cases.This role sits at the core of the company's value proposition-turning data into production-grade AI systems and revenue-generating solutions across industries such as manufacturing, energy, and logistics.ResponsibilitiesInternal AI Platform & MLOps LeadershipDefine and build scalable AI/ML platforms and MLOps infrastructureEstablish end-to-end ML lifecycle (data ingestion - training - deployment - monitoring)Ensure model scalability, reliability, and performance in productionOptimize models for edge environments and low-compute scenariosImplement model monitoring, drift detection, and retraining pipelinesStandardize tools, frameworks, and development practiceAI Solutions & Client Delivery (External)Build and scale AI solution offerings for enterprise clients, including:Lead end-to-end delivery: problem definition - modeling - deploymentDevelop reusable AI frameworks and acceleratorsSupport pre-sales, demos, and client engagementsEnsure solutions are practical, explainable, and ROI-drivenAI Product Development & Industrial InnovationDefine and execute roadmap for AI-powered productsCollaborate with data and platform teams to build scalable solutionsIdentify opportunities to productize reusable AI componentsBuild domain-specific AI solutions tailored for:Ensure products are deployable in low-data, low-infrastructure environmentsCommercial & Strategic ContributionContribute to revenue generation through AI solutions and productsSupport enterprise sales with technical expertise and solution designHelp define pricing models and solution packagingBuild partnerships with AI/cloud ecosystem playersTeam Building & LeadershipBuild and lead a high-performing AI/ML engineering teamBalance research vs applied engineering focus (execution-first mindset)Mentor talent and build strong local AI capabilityFoster a culture of rapid experimentation and production deliveryRequirements10 - 15+ years in AI/ML engineering or applied AI rolesProven experience in deploying AI solutions to production atscaleStrong expertise in:Python, TensorFlow, PyTorchML pipelines and MLOps tools (MLflow, Kubeflow, etc.)Experience with cloud platforms (AWS, Azure,GCP)Strong understanding of data engineering fundamentals PreferredExperience in AI consulting, services, or client deliveryrolesExposure to industrial AI / IoT / sensor data usecasesExperience with edge AI and real-time inference systemsBackground in startups or fast-scaling environmentsKnowledge of AI explainability and model governance