AI Application Engineer

Position Overview

• Location Type: Remote
• Employment Type: Full Time
• Salary: Not provided
• Description: We are seeking a highly skilled and experienced AI Application Engineer to design and build AI applications for customers, deploying them into production. The ideal candidate will have a strong background in software engineering, machine learning, and experience with various AI technologies, including Document Extraction, Conversational AI, Vision AI, NLP, and Gen AI. This role involves fine-tuning and personalizing ML models, developing prompts for LLMs, collaborating with data scientists, and building scalable machine learning pipelines.

Requirements

• At least 5 years of experience in designing and building AI applications for customer deployment.
• Software engineering experience in building secure, scalable, and performant applications for customers.
• Experience with Document extraction using AI, Conversational AI, Vision AI, NLP or Gen AI.
• Hands-on customer experience with RAG solutions or fine-tuning of LLM models.
• Good understanding of natural language processing, computer vision, or other deep learning techniques.
• Expertise in Python, NumPy, Pandas, and various ML libraries (e.g., XGBoost, TensorFlow, PyTorch, Scikit-learn, LangChain).
• Familiarity with Google Cloud or any other Cloud Platform and its machine learning services.
• Excellent communication, collaboration, and problem-solving skills.

Responsibilities

• Design, develop, and operationalize existing ML models by fine-tuning and personalizing them.
• Evaluate machine learning models and perform necessary tuning.
• Develop prompts that instruct LLMs to generate relevant and accurate responses.
• Collaborate with data scientists and engineers to analyze and preprocess datasets for prompt development, including data cleaning, transformation, and augmentation.
• Conduct thorough analysis to evaluate LLM responses and iteratively modify prompts to improve LLM performance.
• Build and deploy scalable machine learning pipelines on GCP or any equivalent cloud platform involving data warehouses, machine learning platforms, dashboards, or CRM tools.
• Experience working with the end-to-end steps involving but not limited to data cleaning, exploratory data analysis, dealing outliers, handling imbalances, analyzing data distributions (univariate, bivariate, multivariate), transforming numerical and categorical data into features, feature selection, model selection, model training and deployment.
• Proven experience building and deploying machine learning models in production environments for real-life applications.

Good to Have

• Google Cloud Certified Professional Machine Learning or TensorFlow Certified Developer certifications or equivalent.
• Experience with one or more public cloud platforms – namely GCP, AWS, or Azure.
• Experience with AutoML and vision techniques.

Company Information

• Not provided