ML Solution Architect
Bogotá, Capital District / Medellín, Antioquia / San Jose
Delivery – Pavel Borobov /
Full-time /
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.
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.
- Experience in Generative AI implementations.
- Proficiency with graph databases (Neo4j, AWS Neptune).
- Hands-on experience with Kubernetes, Docker, and containerized applications.
- Knowledge of data mesh principles and data contracts.
- Operational knowledge of infrastructure deployment tools like AWS CDK, CloudFormation, and Terraform.
Nice to Have:
