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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
- 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 backend/ML services best practices for scalable and maintainable solutions
- 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
- Proven experience with microservice architecture and containerized deployment options
- Hands‑on experience with Kubernetes, Docker, and containerized applications
- Proficiency in backend‑related languages: TS, Java, Python, and others
- Solid understanding of machine learning and MLOps tools (PyTorch, Sage Maker, MLFlow)
- 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, Cloud Formation, and Terraform

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📌 Data/ ML Solution Architect (GenAI, AWS) (Colombia)
🏢 Provectus
📍 Colombia