Senior AI Solutions Engineer

As a Senior AI Solutions Engineer in our pioneering AI team, you will architect and deliver next-generation AI-powered systems by leveraging state-of-the-art generative models and computer vision techniques. Your role is instrumental in transforming cutting-edge research into scalable, production-grade solutions driving innovation across multiple domains, including image synthesis, NLP, and automated content generation. **Key Responsibilities:** - Develop, train, and deploy advanced generative AI architectures such as GANs, VAEs, diffusion models, and large language models for diverse applications. - Integrate computer vision algorithms with generative models to enhance capabilities like object detection, image segmentation, and quality enhancement. - Implement and fine-tune deep learning models using frameworks like PyTorch, TensorFlow, and Hugging Face Transformers. - Design scalable data ingestion and processing pipelines using PySpark, ETL workflows, and big data technologies. - Build and maintain APIs and backend services in Python for smooth integration of AI models into production environments. - Apply NLP techniques for conversational AI, summarization, and semantic understanding tasks. - Utilize Docker and Kubernetes to containerize and orchestrate AI workloads for efficient cloud or hybrid deployment. - Champion best practices in model reliability, version control, CI/CD pipelines, and code quality through rigorous testing and monitoring. - Optimize models for performance and latency to meet real-time application requirements. - Lead experiments, conduct A/B tests, and analyze performance metrics to enhance model accuracy and user experience continuously. **Qualifications Required:** - Bachelors or Masters degree in Computer Science, AI, Data Science, or related discipline; PhD is a plus. - 7+ years of industry experience specializing in Machine Learning, Deep Learning, and AI model engineering. - Deep expertise in generative AI and computer vision with a strong portfolio of projects or contributions. - Proficient in Python and familiar with data libraries such as Pandas and NumPy. - Extensive hands-on experience with ML frameworks including PyTorch, TensorFlow, and transformer architectures. - Skilled in building scalable, robust data pipelines using ETL processes and PySpark. - Experience with NoSQL databases like MongoDB or DynamoDB for handling unstructured data. - Strong track record of deploying ML models using Docker, Kubernetes, and cloud infrastructure. - Solid grasp of software engineering principles, including CI/CD, automated testing, and collaborative code reviews. - Comfortable working in agile environments with multidisciplinary teams. **Bonus Points For:** - Experience with multimodal AI models combining text, image, and audio inputs. - Familiarity with prompt engineering and fine-tuning foundation models. - Contributions to open-source AI projects or a record of published AI research.,