AI /Artificial Intelligence Engineer- Gurgaon

The ideal candidate will have a solid foundation in software engineering, AI/ML technologies, and software development processes. The Candidate will be responsible for guiding a team of 4-5 AI developers, ensuring the delivery of robust AI solutions while maintaining high standards in architecture, coding practices, and project execution. Required AI/ML Skills: Generative AI (GenAI): Experience with Large Language Models (LLMs) like GPT, BERT, or LLaMA. Familiarity with fine-tuning LLMs and integrating them into enterprise applications and Databases. Knowledge of text generation, summarization, translation, and conversational AI. Traditional Machine Learning: Proficiency in ML techniques like supervised learning, unsupervised learning, and reinforcement learning. Hands-on experience with classification, regression, clustering, and time-series forecasting. AI Frameworks and Tools: Proficient in frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn, and Keras. Familiarity with ML pipelines using tools like MLflow, Kubeflow, or TensorFlow Extended (TFX). Deployment: Knowledge of containerization (Docker, Kubernetes) and serverless architectures for scalable AI solutions. Required Software Engineering Skills: Programming Languages: Strong proficiency in Python (preferred), Java, or Go for AI application development. Experience with API development frameworks such as FastAPI, Django, or Flask. Architectural Concepts: Deep understanding of microservices architecture, event-driven design, and RESTful APIs. Knowledge of distributed systems and high-performance computing. Soft Skills: Communication and Presentation: Excellent verbal and written communication skills, with the ability to simplify complex technical concepts for diverse audiences. Strong presentation skills to effectively convey architectural designs and project updates to customers and stakeholders. Team Collaboration: Proven experience in leading and mentoring technical teams, fostering collaboration, and encouraging continuous learning. Ability to work effectively across cross-functional teams including data engineers, product managers, and QA engineers. Key Responsibilities: Technical Leadership: Lead, mentor, and guide a team of AI developers. Ensure adherence to best practices in software engineering, AI model development, and deployment. Review and approve architectural designs, ensuring scalability, performance, and security. AI Application Development: Architect and design AI solutions that integrate both Generative AI (GenAI) and traditional Machine Learning (ML) models. Oversee the end-to-end development lifecycle of AI applications, from problem definition to deployment and maintenance. Optimize model performance, ensure model explainability, and address model drift issues. Architectural Oversight: Develop and present the big-picture architecture of AI applications, including data flow, model integration, and user interaction. Dive deep into individual components, such as data ingestion, feature engineering, model training, and API development. Ensure the AI solutions align with enterprise architectural standards and customer requirements. Customer Engagement: Act as the primary technical interface for customer meetings, providing clear explanations of the AI solutions architecture, design decisions, and project progress. Collaborate with stakeholders to understand business needs and translate them into technical requirements. Ensure proper documentation, including design documents, code reviews, and testing protocols. The ideal candidate will have a solid foundation in software engineering, AI/ML technologies, and software development processes. The Candidate will be responsible for guiding a team of 4-5 AI developers, ensuring the delivery of robust AI solutions while maintaining high standards in architecture, coding practices, and project execution. Required AI/ML Skills: Generative AI (GenAI): Experience with Large Language Models (LLMs) like GPT, BERT, or LLaMA. Familiarity with fine-tuning LLMs and integrating them into enterprise applications and Databases. Knowledge of text generation, summarization, translation, and conversational AI. Traditional Machine Learning: Proficiency in ML techniques like supervised learning, unsupervised learning, and reinforcement learning. Hands-on experience with classification, regression, clustering, and time-series forecasting. AI Frameworks and Tools: Proficient in frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn, and Keras. Familiarity with ML pipelines using tools like MLflow, Kubeflow, or TensorFlow Extended (TFX). Deployment: Knowledge of containerization (Docker, Kubernetes) and serverless architectures for scalable AI solutions. Required Software Engineering Skills: Programming Languages: Strong proficiency in Python (preferred), Java, or Go for AI application development