**ML Solution Architect**: Bogotá, Capital District, Medellín, Antioquia, San Jose **About project** **Provectus **helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. As a Solutions Architect, you will be responsible for designing, planning, and implementing scalable, cloud-based, and on-premise data and ML architectures. You will collaborate with internal teams, clients, and stakeholders to build state-of-the-art solutions across Big Data, machine learning, and real-time analytics environments. Your role will focus on delivering high-quality, innovative solutions while adhering to best practices in architecture, security, and compliance. This role also requires providing strategic technical leadership on complex, high-impact customer engagements. You will design advanced technical solutions, manage technical risks, and collaborate with cross-functional teams to ensure successful solution delivery. Your role will involve driving innovation, optimizing customer KPIs, and mentoring other architects and technical leaders. **Responsibilities**: - Lead the design and implementation of data and AI/ML architecture solutions across cloud and on-premise platforms. - Lead complex customer engagements, providing strategic technical vision and aligning solutions with customer business goals. - Build and maintain strong relationships with key customer stakeholders, acting as a trusted technical advisor. - Lead technical workshops, training sessions, and presentations. - Define and execute data lifecycle processes: ingestion, storage, processing, and visualization. - Develop and maintain streaming data solutions using Lambda/Kappa architectures, Kafka, Spark, and Flink. - Collaborate with business units and stakeholders to align solutions with business goals. - Ensure 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 talent. - Design and execute proofs of concept for emerging technologies like Generative AI, Machine Learning - Drive MLOps best practices for scalable and maintainable machine learning pipelines. - Oversee data governance and data quality processes across platforms. - Stay updated with the latest technology trends and continuously improve the architecture strategy. **Requirements**: - 7+ 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 audiences. - Technical sales or pre-sales experience with cloud and big data and ML solutions. - Strong leadership and team collaboration abilities. - Strategic thinking with a focus on delivering measurable business value. - Proven ability to build strong relationships with customers and act as a trusted advisor. - Proficiency in data engineering and analytics, designing data pipelines and architectures using AWS, GCP or Azure data stack - Strong understanding of AI/ML concepts and experience integrating AI/ML components into solutions. - Proven experience with data lakes, data warehouses, and real-time data analytics. - Proficiency in Java, Python, and modern data technologies like Snowflake and Databricks. - Solid understanding of machine learning and MLOps tools (TensorFlow, PyTorch, SageMaker). - Demonstrated ability to lead and mentor cross-functional teams. - Familiarity with agile methodologies. Nice to Have: - Experience in Generative AI implementations. - Proficiency with graph databases (Neo4j, AWS Neptune). - Knowledge of data mesh principles and data contracts. - Operational knowledge of infrastructure deployment tools like AWS CDK, CloudFormation, and Terraform.