AI/ML Engineer
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AI/ML Engineer (Contract)
Location: Florida (Required) - No Relocation Duration: 6 Months (with possible extension)
Overview:
We are seeking an experienced AI/ML Engineer to support the design, development, and deployment of scalable machine learning and generative AI solutions. This role will focus on building production-ready models, optimizing data pipelines, and collaborating with cross-functional teams to deliver high-impact AI initiatives.
Key Responsibilities:
- Design, develop, and deploy machine learning models and AI solutions in production environments
- Build and optimize LLM-based applications (e.g., RAG pipelines, prompt engineering, agent workflows)
- Develop and maintain data pipelines to support model training, evaluation, and inference
- Implement MLOps best practices, including model versioning, monitoring, and automated retraining
- Collaborate with data engineers, product managers, and stakeholders to translate business requirements into technical solutions
- Evaluate model performance and continuously improve accuracy, scalability, and efficiency
- Ensure compliance with data governance, security, and responsible AI standards
- Required Qualifications6–7 years of experience in AI/ML engineering, data science, or related field
- Strong programming skills in Python (NumPy, Pandas, Scikit-learn)
- Hands-on experience with TensorFlow or PyTorchExperience building and deploying LLM-based solutions (RAG, embeddings, vector databases)
- Solid understanding of data engineering concepts (ETL, data pipelines, distributed processing)
- Experience with cloud platforms (AWS, Azure, or Google Cloud)
- Familiarity with MLOps tools (e.g., MLflow, Airflow, Docker, CI/CD pipelines)
- Strong problem-solving skills and ability to work independently in a fast-paced environment
Preferred Qualifications:
- Experience with Databricks, Snowflake, or similar data platforms
- Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate)
- Exposure to agentic AI frameworks (e.g., LangChain, LlamaIndex)
- Experience working in regulated environments (finance, healthcare, or government)
- Familiarity with AI governance frameworks (e.g., NIST AI RMF, Responsible AI practices)