AI/ML Architect

About Aezion

Aezion is one of the premiere custom software providers in United States. and we live by the adage that our word is our bond. Our Promise is to get it right or make it right. We accomplish this by investing the effort to exceed client expectations from start to finish - architecting, designing, developing, hosting, deploying, maintaining, and supporting our clients throughout the project lifecycle. We believe that work is ministry – an expression of our values. Our goal is to honor our commitments to clients and the life energies of Aezion employees through results that transform clients into lifelong partners.

Position Description

Job Title:

Job Summary:

AI/ML Engineer

Key Responsibilities:

• Data Preparation and Analysis: Ability to understand large datasets, preprocess them, and extract features
• Data Preprocessing Techniques: Knowledge of normalization, feature encoding, and handling missing values
• Data Cleaning: Identifying and rectifying errors, outliers, and missing values within datasets
• Design, develop, and implement machine learning & Deep Learning (FNN, CNN, RNN) models, with a focus on LLMs, generative AI, and fraud detection systems.
• Deploy and maintain ML models in AWS or any other cloud environments.
• Optimize model performance and scalability.
• Collaborate with cross-functional teams to integrate AI solutions into existing applications.
• Develop and maintain APIs (RESTful) for AI model integration.
• Implement MLOps best practices to streamline the ML lifecycle.
• Stay up-to-date with the latest advancements in AI/ML and incorporate new techniques into our workflow.
• Develop and implement fraud detection models to identify and prevent fraudulent activities.
• Evaluate model performance using appropriate metrics and techniques, ensuring high accuracy and reliability.
• Experience with Machine Learning Libraries and Frameworks: Familiarity with tools like TensorFlow, PyTorch, and scikit-learn, Keras

We are seeking a talented Generative AI Developer to join our innovative team. The ideal candidate will have a strong background in AI and machine learning, with a focus on generative models and large language models (LLMs). You will work closely with cross-functional teams to conceptualize, design, test, and deploy AI projects that drive innovation and provide value in the rapidly evolving field of artificial intelligence. Join us and be part of a dynamic team that is shaping the future of AI.

• Advanced Programming Knowledge: Mastery in programming languages like Python and expertise in AI-specific libraries such as TensorFlow, PyTorch, and Keras. Proficiency in implementing and manipulating complex algorithms essential for generative AI development.
• Generative Models Expertise: In-depth experience with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Ability to design, train, and optimize these models for generating high-quality and creative content.
• Natural Language Processing (NLP): Strong background in text generation techniques, including text parsing, sentiment analysis, and the use of transformers like GPT models for advanced text-based applications.
• Vector Databases: Hands-on experience with vector databases such as Pinecone, PgVector, and Qdrant for efficient retrieval and similarity search.
• Embedding, Retrieval-Augmented Generation (RAG), and Indexing: Expertise in creating embeddings, implementing RAG workflows, and indexing vector databases to improve search, retrieval, and contextual generation.
• LangChain and LangGraph: Expertise in building advanced workflows and applications using LangChain for LLM-based solutions and LangGraph for structured workflows with conditional logic.
• FastAPI and Microservices: Proficiency in building scalable, RESTful FastAPI applications and microservices architectures for deploying AI solutions.
• LangSmith and LangFuse: Experience using LangSmith for debugging and evaluation of LLM chains, and LangFuse for logging and analytics in LLM applications.
• Cloud Computing and Deployment: Expertise in deploying and managing AI applications on cloud platforms such as AWS, Google Cloud, and Microsoft Azure. Familiarity with Docker for containerization and Kubernetes for scaling and orchestration.