Machine Learning Solutions Architect

Provectus helps companies adopt AI to transform how they operate, compete, and drive value.

The company focuses on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting suitable 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.

Join us if you have the same passion for making products using AI/ML technologies, cloud services, and data engineering.

Join us if you value a friendly corporate environment and almost a family-driven approach to every employee.

Join us if you want to grow professionally and be ready to learn new things constantly.

As an ML Solution Architect, you’ll be provided with all opportunities for development and growth.

Responsibilities:

Provide technical support on pre-sales and sales calls with new customers, including investigating business problems, representing Provectus technical expertise, and offering solutions. Conduct and drive technical workshops for new and existing customers.

Create machine learning solutions, including problem framing, exploratory data analysis, dataset preparation, model & algorithm development, model training & evaluation, model deployment, monitoring, and maintenance.

Implement architecture to deploy machine learning pipelines and CI/CD processes.

Estimate development, maintenance, and other costs for the proposed solutions.

Act as a technical liaison between customers and teams to provide customer-driven product improvement feedback.

Stay abreast of new tools, packages, and Machine Learning techniques while consistently pushing the limit of what is possible to deliver the best solutions for clients.

Evangelize ML services and share best practices through blogs, white papers, reference architectures, public-speaking events, etc.

Requirements:

Experience in designing and implementing models or machine learning algorithms in production, including problem framing, exploratory data analysis, dataset preparation, model & algorithm development, model training & evaluation, model deployment, integration, monitoring, and maintenance.

Consulting experience and track record of helping customers with their AI needs.

Ability to lead exploratory data analysis within new subject matter experts or new data sources.

Strong knowledge of Python – able to write production-level code that is well-written and explainable.

Experience with SQL, NumPy, pandas, scikit-learn, HuggingFace transformers, and MLFlow.

Knowledge of MLOps or CI/CD best practices.

Preferred skills:

Industrial experience with LLM and related frameworks (LangChain, LLamaIndex, etc.)

Practical experience with AWS (or alternatives from other major clouds):

- Amazon SageMaker.

- Amazon Redshift, EMR, or Glue.

- Core services: S3, networking, IAM, ECR.

Knowledge of Apache Spark or Ray framework.

Practical experience with at least one deep learning framework like PyTorch or TensorFlow.

Full-time