AI Engineer

Role Overview: As an AI Engineer, your primary responsibility will be to architect, develop, and deploy advanced AI solutions covering Machine Learning, Generative AI, NLP, and LLMs. You will need to stay updated on the latest AI advancements, actively research and integrate emerging trends, and technologies like LLMOps, Large Model Deployments, LLM Security, and Vector Databases. Collaborating closely with cross-functional teams, including business units, accounts teams, researchers, and engineers, will be essential to translate business requirements into actionable AI solutions. Additionally, you must exhibit expertise in responsible AI practices to ensure fairness, transparency, and interpretability in all models. Identifying and mitigating potential risks related to AI and LLM development and deployment will be crucial for emphasizing data trust and security. Moreover, contributing to the professional growth of the AI team by mentoring engineers, fostering knowledge sharing, and promoting a culture of continuous learning will be part of your role. This position is based in a lab environment and involves hands-on, fast-paced, and high-intensity work, requiring you to be proactive, adaptable, and comfortable working in a dynamic and demanding setting. Key Responsibilities: - Architect, develop, and deploy advanced AI solutions encompassing Machine Learning, Generative AI, NLP, and LLMs. - Remain updated on the latest AI advancements and integrate emerging trends and technologies like LLMOps, Large Model Deployments, LLM Security, and Vector Databases. - Streamline data modeling processes to automate tasks, enhance data preparation, and facilitate data exploration to optimize business outcomes. - Collaborate closely with cross-functional teams to translate business requirements into actionable AI solutions. - Exhibit expertise in responsible AI practices to ensure fairness, transparency, and interpretability in all models. - Identify and mitigate potential risks related to AI and LLM development and deployment, focusing on data trust and security. - Contribute to the professional development of the AI team by mentoring engineers, fostering knowledge sharing, and promoting a culture of continuous learning. Qualifications: - Minimum of 2 years of hands-on experience in developing and deploying AI solutions with a proven track record of success. - Masters degree in Computer Science, Artificial Intelligence, or a related field (or equivalent experience). - Proficiency in Machine Learning, NLP, Generative AI, and LLMs, including their architectures, algorithms, and training methodologies. - Understanding of LLMOps principles, Prompt Engineering, In-Context Training, LangChain, and Reinforcement Learning. - Familiarity with best practices for large model deployment, monitoring, management, and scalability. - Experience with Azure Cloud services. - Strong communication, collaboration, and problem-solving abilities. - Commitment to ethical AI practices and security standards. - Proficiency in deep learning frameworks and languages such as Azure ML platform, Python, PyTorch, etc. - Hands-on experience with ML frameworks, libraries, and third-party ML models. - Expertise in building solutions using AI/ML/DL open-source tools and libraries. - Strong analytical and problem-solving skills. - Ability to write optimized and clear code and address complex technical challenges effectively. - Self-motivated and fast learner with a proactive approach to learning new technologies. - Proficiency in data analysis and troubleshooting skills. - Experience in building AI/ML/DL solutions for NLP/text applications, with familiarity in reinforcement learning being advantageous. - Minimum of 2 years of experience on AI/ML/DL projects, with specialization or certification in Artificial Intelligence being a plus. - Good knowledge of Azure AI/Cognitive Services tools. Role Overview: As an AI Engineer, your primary responsibility will be to architect, develop, and deploy advanced AI solutions covering Machine Learning, Generative AI, NLP, and LLMs. You will need to stay updated on the latest AI advancements, actively research and integrate emerging trends, and technologies like LLMOps, Large Model Deployments, LLM Security, and Vector Databases. Collaborating closely with cross-functional teams, including business units, accounts teams, researchers, and engineers, will be essential to translate business requirements into actionable AI solutions. Additionally, you must exhibit expertise in responsible AI practices to ensure fairness, transparency, and interpretability in all models. Identifying and mitigating potential risks related to AI and LLM development and deployment will be crucial for emphasizing data trust and security. Moreover, contributing to the professional growth of the AI team by mentoring engineers, fostering knowledge sharing, and promoting a culture of continuous learning will be part of your role. This position is bas