Senior Python AI/ML Engineer

• Tampa or Dallas
• $62hr c2c PAY
• 6 months CTH
• USC/GC
• Tampa, FL / Dallas, TX (Hybrid) - Need to be local

Enterprise AI Engineering | Cloud | MLOps | CI/CD

Job Description:

Position Summary

We are seeking a highly skilled Senior Consultant - Python AI/ML Engineer to design, build, and deploy scalable enterprise AI solutions. The ideal candidate combines strong Python engineering skills with hands-on experience in machine learning, Generative AI, cloud platforms, API development, MLOps, and CI/CD automation. The role partners with business stakeholders, architects, data engineers, product teams, and DevOps teams to deliver secure, reliable, and production-ready AI-enabled applications.

Key Responsibilities

AI/ML and Generative AI Development

· Design, develop, test, and deploy machine learning and Generative AI solutions for enterprise use cases.

· Build and optimize end-to-end AI/ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.

· Develop Retrieval-Augmented Generation (RAG) solutions and integrate large language models into enterprise applications.

· Perform model evaluation, performance tuning, prompt optimization, and ongoing solution improvement.

Python and API Engineering

· Develop scalable backend services, microservices, and RESTful APIs using Python.

· Create reusable frameworks, libraries, and integration components.

· Write clean, secure, maintainable, and production-quality code using modern engineering standards.

· Implement unit, integration, regression, and performance testing.

Data Engineering and Integration

· Design data pipelines for structured and unstructured data processing.

· Integrate AI/ML models with internal and external enterprise systems through APIs and event-driven patterns.

· Work with cloud data platforms such as Snowflake, Databricks, or comparable technologies.

· Collaborate with data engineering teams to support data quality, lineage, governance, and security requirements.

MLOps, DevOps and CI/CD

· Build and maintain CI/CD pipelines for application, infrastructure, and model deployments.

· Automate build, test, code-quality checks, security scanning, deployment, and release processes.

· Implement MLOps practices including model versioning, experiment tracking, monitoring, drift detection, and retraining.

· Use tools such as Jenkins, GitHub Actions, Azure DevOps, Bitbucket Pipelines, Docker, and Kubernetes.

Cloud Engineering

· Design and deploy cloud-native solutions using AWS, Azure, or GCP.

· Use containers, orchestration platforms, and managed cloud services to deliver scalable and resilient solutions.

· Apply enterprise security, observability, access-control, and high-availability standards.

Collaboration and Technical Leadership

· Translate business requirements into practical technical designs and implementation plans.

· Partner with product owners, architects, data engineers, DevOps teams, and business stakeholders.

· Lead design reviews, code reviews, troubleshooting, and technology evaluations.

· Mentor engineers, promote engineering best practices, and contribute to Agile delivery ceremonies.

Required Qualifications

· Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent professional experience.

· 8-12 years of software engineering experience, including substantial hands-on Python development.

· Production experience designing, deploying, and supporting AI/ML solutions.

· Strong understanding of machine learning algorithms, model lifecycle management, and MLOps principles.

· Experience developing RESTful APIs, microservices, and system integrations.

· Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.

· Demonstrated experience creating and maintaining CI/CD pipelines and applying DevOps practices.

· Experience with Docker, Kubernetes, SQL, Git, automated testing, debugging, and production support.

· Strong analytical, communication, stakeholder-management, and problem-solving skills.

Preferred Qualifications

· Experience with Generative AI, large language models, prompt engineering, RAG, embeddings, and vector databases.

· Hands-on knowledge of frameworks such as LangChain, LlamaIndex, Semantic Kernel, scikit-learn, TensorFlow, or PyTorch.

· Experience with Snowflake, Databricks, Spark, PySpark, MLflow, Kubeflow, or Airflow.

· Experience building cloud-native applications using FastAPI or Flask.

· Experience in financial services, audit, risk, compliance, or another highly regulated enterprise environment.

· Knowledge of responsible AI, model governance, data privacy, security, and enterprise control requirements.

Core Technical Skills

Success Measures

· Deliver scalable, secure, and supportable AI/ML solutions into production.

· Establish reliable CI/CD and MLOps practices that improve deployment quality and repeatability.

· Create measurable business value through AI-enabled applications, automation, and data-driven capabilities.

· Raise engineering standards through technical leadership, mentoring, documentation, and reusable solution patterns.