Senior Python AI/ML Engineer
About The Position
We are seeking a highly skilled Senior Consultant - Python AI/ML Engineer to design, build, and deploy scalable enterprise AI solutions. The role combines Python engineering with hands-on machine learning, Generative AI, cloud platforms, API development, MLOps, and CI/CD automation. The engineer partners with business stakeholders, architects, data engineers, product teams, and DevOps teams to deliver secure, reliable, production-ready AI-enabled applications. The position is hybrid in Tampa, Florida, or Dallas, Texas, and requires the candidate to be local. The engagement is described as consultant/contract with a six-month contract-to-hire arrangement. The role covers AI/ML and Generative AI development, Python and API engineering, data engineering and integration, MLOps and DevOps, cloud engineering, and technical collaboration and leadership. The engineer will develop production solutions and pipelines, integrate models into enterprise systems, automate delivery and model operations, and help establish engineering standards. Success is measured by production-ready solutions, reliable CI/CD and MLOps, measurable business value, and contributions to engineering quality and team capability.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
- Develop scalable backend services, microservices, and RESTful APIs using Python.
- Create reusable frameworks, libraries, and integration components.
- Write clean, secure, maintainable, production-quality code using modern engineering standards.
- Implement unit, integration, regression, and performance testing.
- 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.
- Collaborate with data engineering teams to support data quality, lineage, governance, and security requirements.
- 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.
- 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.
- 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.
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What This Job Offers
Career Level
Senior
