Description
ML Solution Architect
Team: SA practice - Pavel Borobov
Location: Medellín, Antioquia, Bogotá, Capital District, Cali, Valle del Cauca, Costa Rica, Barranquilla, Bucaramanga, Santander
Commitment: Full-time
Workplace Type: remote
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.
With offices in North America, LATAM, and EMEA, Provectus partners with clients around the world and is obsessed with leveraging cloud, data, and AI to reimagine the way clients operate & compete.
Position Summary:
- As a Solutions Architect, you will be responsible for designing, planning, and implementing scalable, cloud-based, and on-premise data and ML architectures and backend services.
- 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.
- As a Solutions Architect, you will provide 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.
- 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 any of backend backend-related languages: TS, Java, Python, and others.
- Solid understanding of machine learning and MLOps tools (PyTorch, SageMaker, MLFlow).
- Demonstrated ability to lead and mentor cross-functional teams.
- Familiarity with agile methodologies.
- 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.
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