The Role We are seeking a Solutions Architect to join our Engineering Team.
This role combines deep hands-on engineering capability in Python, machine learning frameworks, and data pipelines with technical leadership in designing, developing, deploying, and optimizing production-grade machine learning models and solutions.
The adecuado candidate is a senior technical leader who can define end-to-end ML solution architecture, lead model development, evaluation, deployment, monitoring, and production support, and ensure adherence to ML engineering and MLOps best practices while delivering reliable, scalable, and maintainable solutions in a fast-moving environment.
This position also requires strong collaboration and leadership skills.
The Solutions Architect must work effectively with data scientists, engineers, product stakeholders, clients, and delivery teams, while providing technical guidance, mentoring others, and driving high engineering quality.
The ideal candidate should be proactive, pragmatic, and able to balance hands-on implementation with strategic technical decision-making.
Key Responsibilities Solution Architecture: Lead the architecture of machine learning solutions end-to-end, from data ingestion and feature engineering to model serving and monitoring, ensuring scalability, security, and maintainability Model Development: Design, develop, and optimize machine learning models using Python and ML frameworks, while leading evaluation and adoption of new tools, algorithms, and methodologies to improve company standards Data Pipelines:



Design and oversee reliable batch and streaming data pipelines and feature stores that feed training and inference, ensuring data quality, lineage, and reproducibility Model Evaluation: Define evaluation strategies, metrics, validation approaches, and experiment tracking to ensure models meet accuracy, fairness, and business performance targets before release Deployment lead incident triage, root-cause analysis, and retraining decisions Best Practices: Establish and ensure adherence to ML engineering best practices, including code quality, testing, reproducibility, documentation, and responsible-AI and data-privacy requirements Cross Functional Collaboration: Partner with business ops, stakeholders, and clients to translate business problems into ML solutions and act as the intermediary between business operations, data science, and engineering Team Development: Provide technical guidance and mentor engineers and data scientists at all levels while also leading training sessions, design and code reviews, providing constructive feedback, and aligning technical standards across the team Soft Skills Business Acumen: Connect ML architecture and modeling decisions to business outcomes, anticipating impacts on cost, risk,



and value, and providing decisions to maximize long-term value.
Accountability: Accountable for the technical and delivery success of ML solutions or projects, taking ownership of outcomes across teams and addressing issues proactively rather than reactively.
Communication: Communicate complex ML concepts, model behavior, and trade-offs clearly to both technical and non-technical audiences, aligning stakeholders, and enabling confident decision making.
Judgement: Demonstrate judgment by making high-impact decisions, balancing experimentation and short-term delivery with long-term sustainability, and escalating risks early.
Collaboration: Drive alignment across multiple teams and disciplines by acting as a unifying technical leader, resolving cross-team friction.
Curiosity: Maintain curiosity about the evolving ML landscape and emerging technologies, using that understanding to anticipate challenges, guide innovation, and continuously improve technical and delivery practices.
Required Qualifications Experience: Over 6+ years of software or data engineering experience, including 3+ years in an architect or technical lead role and 4+ years delivering machine learning solutions to production, in the following: Tech Stack Languages SQL; Scala or Java a plus NumPy, pandas, Polars scikit-learn, XGBoost, LightGBM, CatBoost Deep Learning Frameworks PyTorch, TensorFlow / Keras Hugging Face Transformers Model optimization: ONNX, quantization, distillation Data Engineering

📌 Solutions Architect - Machine Learning (Xico)
🏢 Capmation
📍 Xico