AI/ML Engineer

Responsibilities

Generative AI Application Development

Build and deploy Generative AI solutions using techniques like Retrieval-Augmented Generation (RAG) and Agentic AI Workflows.

Apply prompt engineering, LLM fine-tuning, and evaluation strategies to optimize model performance.

Integrate AI solutions into production systems, collaborating with engineering and product teams.

Model Development and Deployment

Design, train, and optimize machine learning models to address business needs.

Deploy models in production environments ensuring scalability, reliability, and maintainability.

Monitor and improve models based on performance metrics and user feedback.

Data Management

Collect, clean, and preprocess large-scale datasets for model training and evaluation.

Apply feature engineering and data augmentation techniques to boost model effectiveness.

Build and maintain data pipelines using tools like Snowflake (Streams, Tasks, UDFs).

Python API Development

Build asynchronous, scalable Python APIs (FastAPI, Gunicorn, Uvicorn) for model inference and integration.

Research and Innovation

Stay up to date with the latest AI research and industry trends, especially in Generative AI and LLMs.

Explore and prototype new AI methodologies that drive innovation.

Contribute to technical roadmaps and innovation strategy.

Collaboration and Communication

Partner with engineers, data scientists, and product managers to align AI efforts with business objectives.

Communicate technical findings clearly to both technical and non-technical audiences.

Mentor junior engineers and promote a culture of continuous learning.

Minimum Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.

6+ years of hands-on experience in Machine Learning and Data Science.

Strong programming skills in Python.

Experience working with large datasets, data preprocessing, and data analysis.

Proficiency in ML and AI frameworks: TensorFlow, PyTorch, scikit-learn, LangChain.

Solid foundation in statistical methods, data structures, and algorithms.

Familiarity with prompt engineering, LLM fine-tuning, and evaluation techniques.

Proven experience deploying ML models in production environments.

Experience developing Python APIs for model inference.

Preferred Qualifications

Experience with Generative AI architectures (e.g., RAG, Agentic Workflows).

Experience with Snowflake Data Pipelines (Streams, Tasks, UDFs) and Cortex.

Experience with AWS SageMaker, Bedrock, and other AWS cloud AI services.

Familiarity with asynchronous API frameworks (FastAPI, Gunicorn, Uvicorn).

Knowledge of data visualization tools like Matplotlib, Seaborn, or Tableau.

Previous experience in a similar role or industry.

Excellent communication and collaboration skills.

Strong problem-solving ability and passion for continuous learning.