Job Overview The Senior AI/ML Engineer leads the design and productionization of complex machine learning systems that bridge data science and enterprise applications at scale. They architect feature platforms, training and serving systems, evaluation frameworks, and AI infrastructure while integrating ML models into production environments. They mentor engineers, establish technical standards, and partner with data architects, software engineers, product teams, and customer stakeholders to deliver AI systems that are reliable, scalable, secure, and business-driven.

Core Responsibilities

- Lead the design and development of complex machine learning systems, including predictive, prescriptive, classical, deep learning, and ensemble models.

- Senior: Architects enterprise-scale ML platforms while balancing accuracy, latency, scalability, and cost.

- Lead the design and operation of enterprise feature platforms using Snowpark Feature Store.

- Senior: Establishes feature engineering standards, governance, versioning, and reusable feature platform patterns.

- Design, optimize, and operate model training, inference, and serving infrastructure for production ML systems.

- Senior: Defines serving architectures, SLAs, deployment strategies, and performance optimization standards.

- Design and implement model evaluation frameworks, including golden datasets, A/B testing, shadow deployments, drift detection, fairness evaluation, and continuous model validation.

- Senior: Establishes evaluation strategies and production monitoring frameworks across multiple engagements.

- Lead the integration of machine learning solutions across Snowflake-native platforms and AWS cloud services.

- Senior: Defines enterprise AI architecture while aligning engineering teams around deployment best practices.





- Establish MLOps, Responsible AI, governance, monitoring, privacy, security, and compliance standards for production AI systems.

- Senior: Defines operational standards, guardrails, model governance, and AI risk management practices.

- Mentor engineers and elevate AI engineering capabilities through architecture reviews, code reviews, and technical leadership.

- Senior: Coaches junior and mid-level engineers while establishing engineering best practices across teams.

Experience

- 8+ years of experience in AI/ML engineering with multiple production machine learning systems successfully delivered

- Demonstrated experience leading end-to-end ML initiatives from architecture through deployment and production operations

- Extensive experience productionizing classical machine learning and deep learning systems

- Experience working within regulated industries such as Financial Services, Healthcare, or Energy is preferred

- Experience collaborating with architects, data scientists, software engineers, product managers, and business stakeholders

- Experience operating production AI systems including monitoring, evaluation, incident response, and performance optimization

- 5+ years of hands-on experience with classical machine learning and deep learning frameworks

- 2+ years of production experience with Snowpark ML and Snowpark Feature Store





- 2+ years of experience with AWS SageMaker or equivalent ML platforms

- 1+ year of hands-on experience with AWS Bedrock and foundation model integration

- Experience with Snowpark Container Services for production AI workloads

- Production experience implementing MLflow and ML observability platforms such as LangSmith, Arize, or Weights & Biases

Skills

- Expert Python programming for machine learning platforms and AI applications

- Deep expertise in classical machine learning, deep learning, time-series forecasting, and computer vision

- Strong proficiency with PyTorch and/or TensorFlow

- Deep expertise with Snowflake Cortex AI, Snowpark ML, Snowpark Feature Store, and Snowflake-native AI development

- Strong architecture experience designing feature stores, training pipelines, model registries, and serving infrastructure

- Expertise designing REST APIs, FastAPI, gRPC services, and scalable model serving architectures

- Advanced knowledge of model evaluation strategies, experimentation, drift detection, fairness evaluation, and continuous validation

- Strong MLOps expertise including CI/CD for ML, deployment automation, model registries, monitoring, and observability

- Strong understanding of Responsible AI, model governance, AI safety, bias mitigation, hallucination mitigation, privacy, and security

- Ability to architect highly scalable, resilient, and cost-efficient AI platforms

- Strong experience integrating cloud data platforms such as Snowflake and Databricks into ML ecosystems

- Strong technical leadership, mentoring, architecture review, and code review experience

- Excellent communication skills with executives, customers, and cross-functional engineering teams

📌 AI/ML Engineer (Colombia)
🏢 evolv Consulting
📍 Colombia