AI Architect

Position Description

Job Title:AI Architect

Job Summary:

We are looking for an innovative AI Architect to join our team and work on cutting-edge machine learning and artificial intelligence projects. The ideal candidate will have experience in building, deploying, and optimizing AI/ML models, along with a strong foundation in data science, programming, and algorithms. You will help drive the development of intelligent systems that leverage machine learning to solve real-world problems and improve business outcomes.

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

GenAI Developer Job Description

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.