Senior Python AI/ML Engineer

Veriipro; Tampa (FL); Sur place; USD 150 000 - 190 000; Plein temps

Résumé du poste

Veriipro is seeking an experienced AI/ML Engineer to design, build, and deploy enterprise AI solutions. You'll develop end-to-end ML pipelines, integrate RAG and LLMs, and deliver scalable Python applications and RESTful services.

You'll work across data engineering, platform, and business teams to ensure secure, reliable deployments on cloud platforms (AWS/Azure/GCP) using Docker and Kubernetes, while applying MLOps practices and robust testing.

Qualifications

• Bachelor's or master's in CS/engineering/data science or equivalent experience.
• 8–12 years of software engineering experience with hands-on Python.
• Production experience developing and deploying AI/ML solutions.
• Strong ML algorithms, evaluation, and MLOps knowledge.
• Experience building RESTful APIs and enterprise integrations.
• Hands-on with at least one cloud platform: AWS, Azure, or GCP.
• Experience with CI/CD, Docker, Kubernetes, SQL, Git, and automated testing.
• Strong debugging, analytical, and communication skills.

Responsabilités

• Design, develop, test, and deploy AI/ML and Generative AI solutions for enterprise apps.
• Build end-to-end ML pipelines: ingestion, feature engineering, training, evaluation, deployment, monitoring.
• Develop RAG solutions and integrate LLMs into enterprise systems.
• Build scalable Python apps, backend services, microservices, and RESTful APIs.
• Design data pipelines and integrate structured and unstructured data across platforms.
• Implement CI/CD pipelines, automated testing, and secure deployment workflows.
• Apply MLOps: model versioning, experiments, monitoring, drift detection, retraining.
• Deploy cloud-native apps using AWS, Azure, or GCP, with Docker and Kubernetes.
• Collaborate with data engineering, platform, and business teams for quality and reliability.

Connaissances

Python development

RESTful APIs

MLOps

Cloud platforms

CI/CD pipelines

Docker

Kubernetes

Data pipelines

Model evaluation

Debugging

Formation

Bachelor's or Master's in CS/Engineering/Data Science

Outils

Snowflake

Databricks

Spark

PySpark

MLflow

Kubeflow

Airflow

FastAPI

Flask

SQL

Git

scikit-learn

TensorFlow

PyTorch

LangChain

LlamaIndex

Description du poste

Key Responsibilities

• Design, develop, test, and deploy AI/ML and Generative AI solutions for enterprise applications.
• Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
• Develop RAG solutions and integrate large language models into enterprise systems.
• Build scalable Python applications, backend services, microservices, and RESTful APIs.
• Design data pipelines and integrate structured and unstructured data across enterprise platforms.
• Implement CI/CD pipelines, automated testing, and secure deployment workflows.
• Apply MLOps practices, including model versioning, experiment tracking, monitoring, drift detection, and retraining.
• Deploy cloud-native applications using AWS, Azure, or GCP, with Docker and Kubernetes.
• Collaborate with data engineering, platform, and business teams to ensure solution quality, security, scalability, and reliability.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.
• 8–12 years of software engineering experience, including substantial hands-on Python development.
• Production experience developing and deploying AI/ML solutions.
• Strong understanding of machine learning algorithms, model evaluation, and MLOps principles.
• Experience building RESTful APIs, microservices, and enterprise integrations.
• Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.
• Experience with CI/CD pipelines, DevOps practices, Docker, Kubernetes, SQL, Git, and automated testing.
• Strong debugging, analytical, communication, and problem-solving skills.

Preferred Qualifications

• Experience with Generative AI, LLMs, RAG, prompt engineering, embeddings, and vector databases.
• Familiarity with LangChain, LlamaIndex, Semantic Kernel, scikit-learn, TensorFlow, or PyTorch.
• Experience with Snowflake, Databricks, Spark, PySpark, MLflow, Kubeflow, or Airflow.
• Experience developing APIs using FastAPI or Flask.
• Knowledge of responsible AI, model governance, data privacy, and enterprise security requirements.
• Experience in financial services or other regulated enterprise environments.