Data/ Ml Solution Architect (Genai, Aws) (Cauca)

ResponsibilitiesLead the design and implementation of data and AI/ML architecture solutions across cloud and on-premise platformsLead complex customer engagements, providing strategic technical vision and aligning solutions with customer business goalsBuild and maintain strong relationships with key customer stakeholders, acting as a trusted technical advisorLead technical workshops, training sessions, and presentationsDefine and execute data lifecycle processes: ingestion, storage, processing, and visualizationCollaborate with business units and stakeholders to align solutions with business goalsEnsure solutions adhere to security, compliance, and architecture frameworks (e.G., AWS Well-Architected, GCP Architecture Framework)Lead cross-functional teams, providing mentorship and guidance to technical talentDesign and execute proofs of concept for emerging technologies like Generative AI, Machine LearningDrive backend/ML services best practices for scalable and maintainable solutionsOversee data governance and data quality processes across platformsStay updated with the latest technology trends and continuously improve the architecture strategyRequirements7+ years of experience in solutions architecture, with a strong focus on Big Data and cloud platforms (AWS, GCP, Azure)Excellent communication and problem‐solving skills, with the ability to work across multiple projects and the ability to articulate complex technical concepts to both technical and non‐technical audiencesTechnical sales or pre‐sales experience with cloud and big data, and ML solutionsStrong leadership and team collaboration abilitiesStrategic thinking with a focus on delivering measurable business valueProven ability to build strong relationships with customers and act as a trusted advisorProficiency in data engineering and analytics, designing data pipelines and architectures using AWS, GCP, or Azure data stackStrong understanding of AI/ML concepts and experience integrating AI/ML components into solutionsProven experience with data lakes, data warehouses, and real‐time data analyticsProven experience with microservice architecture and containerized deployment optionsHands‐on experience with Kubernetes, Docker, and containerized applicationsProficiency in backend‐related languages: TS, Java, Python, and othersSolid understanding of machine learning and MLOps tools (PyTorch, SageMaker, MLFlow)Demonstrated ability to lead and mentor cross‐functional teamsFamiliarity with agile methodologiesNice to HaveExperience in Generative AI implementationsProficiency with graph databases (Neo4j, AWS Neptune)Knowledge of data mesh principles and data contractsOperational knowledge of infrastructure deployment tools like AWS CDK, CloudFormation, and Terraform#J-*****-Ljbffr Postúlate en Kit Empleo: kitempleo.com.co/empleo/1atkf0