Job Title: Senior Python AI/ML Engineer
Location: Tampa, FL / Dallas, TX (Hybrid)
Duration: Long Term
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 Google Cloud Platform.
- 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 Google Cloud Platform.
- 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
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Category
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Skills
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Programming
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Python, SQL, shell scripting
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AI/ML and GenAI
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scikit-learn, TensorFlow, PyTorch, LLMs, RAG, embeddings, vector databases
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APIs and Services
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REST APIs, FastAPI, Flask, JSON, microservices, event-driven integration
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Cloud and Data
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AWS, Azure or Google Cloud Platform; Snowflake, Databricks, Spark or PySpark
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DevOps and MLOps
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Jenkins, GitHub Actions, Azure DevOps, Bitbucket Pipelines, Git, Docker, Kubernetes, model monitoring
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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.
