Artificial Intelligence Engineer

As an AI/ML Engineer at our company, your role involves designing, developing, and deploying AI/ML models for real-world applications. You will work on natural language processing (NLP), computer vision, or generative AI projects as required. Additionally, you will develop and optimize LLM-powered applications such as chatbots, summarization, and reasoning tools. Your responsibilities will also include building and maintaining data pipelines for training and inference workflows and collaborating with product, data, and engineering teams to integrate AI solutions into business applications. Key Responsibilities: - Design, develop, and deploy AI/ML models for real-world applications. - Work on NLP, computer vision, or generative AI projects. - Develop and optimize LLM-powered applications like chatbots and reasoning tools. - Build and maintain data pipelines for training and inference workflows. - Collaborate with cross-functional teams to integrate AI solutions into business applications. - Perform model evaluation, tuning, and validation for accuracy and reliability. - Implement MLOps/CI-CD pipelines for deploying and monitoring models at scale. - Ensure compliance with AI ethics, fairness, bias reduction, and security best practices. Qualifications Required: - Bachelor's/Master's degree in Computer Science, AI/ML, Data Science, or related field. - 5+ years of experience in machine learning, deep learning, or applied AI engineering. - Strong programming skills in Python (TensorFlow, PyTorch, Scikit-learn). - Hands-on experience with LLMs, NLP, or generative AI. - Solid understanding of data structures, algorithms, and statistical modeling. - Experience with REST APIs, microservices, or cloud deployments. The company prefers candidates with the following qualifications: - Knowledge of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). - Experience with transformer models (BERT, GPT, LLaMA, etc.). - Familiarity with vector databases (Pinecone, Weaviate, FAISS). - Proficiency in MLOps tools (MLflow, Kubeflow, Airflow). - Exposure to data pipelines, big data frameworks (Spark, Hadoop). - Strong analytical mindset and ability to think from a customer/business perspective. As an AI/ML Engineer at our company, your role involves designing, developing, and deploying AI/ML models for real-world applications. You will work on natural language processing (NLP), computer vision, or generative AI projects as required. Additionally, you will develop and optimize LLM-powered applications such as chatbots, summarization, and reasoning tools. Your responsibilities will also include building and maintaining data pipelines for training and inference workflows and collaborating with product, data, and engineering teams to integrate AI solutions into business applications. Key Responsibilities: - Design, develop, and deploy AI/ML models for real-world applications. - Work on NLP, computer vision, or generative AI projects. - Develop and optimize LLM-powered applications like chatbots and reasoning tools. - Build and maintain data pipelines for training and inference workflows. - Collaborate with cross-functional teams to integrate AI solutions into business applications. - Perform model evaluation, tuning, and validation for accuracy and reliability. - Implement MLOps/CI-CD pipelines for deploying and monitoring models at scale. - Ensure compliance with AI ethics, fairness, bias reduction, and security best practices. Qualifications Required: - Bachelor's/Master's degree in Computer Science, AI/ML, Data Science, or related field. - 5+ years of experience in machine learning, deep learning, or applied AI engineering. - Strong programming skills in Python (TensorFlow, PyTorch, Scikit-learn). - Hands-on experience with LLMs, NLP, or generative AI. - Solid understanding of data structures, algorithms, and statistical modeling. - Experience with REST APIs, microservices, or cloud deployments. The company prefers candidates with the following qualifications: - Knowledge of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). - Experience with transformer models (BERT, GPT, LLaMA, etc.). - Familiarity with vector databases (Pinecone, Weaviate, FAISS). - Proficiency in MLOps tools (MLflow, Kubeflow, Airflow). - Exposure to data pipelines, big data frameworks (Spark, Hadoop). - Strong analytical mindset and ability to think from a customer/business perspective.