Senior AI Engineer

Description -Design, build, test, and deploy robust and scalable AI/ML models and applications. Take ownership of features from initial concept through to production and ongoing maintenance.-Contribute to the technical and architectural design of new AI systems and services, ensuring they meet standards for performance, security, and scalability.-Implement state-of-the-art machine learning and deep learning models. Fine-tune and optimize models for specific business use cases, focusing on accuracy and efficiency.-Analyze complex business requirements and translate them into well-architected, practical, and effective AI solutions.-Uphold high standards for code quality, testing, and documentation. Champion software engineering best practices within the team. Deliver code that is secure, reliable and supportable.-Provide technical guidance and mentorship to junior engineers, assisting with code reviews and sharing knowledge to elevate the team's overall capabilities.-Demonstrate a strong curiosity for leveraging AI to improve personal and team productivity. Actively find and implement AI-powered tools and workflows to make your own role and development processes more efficient. Effectively use AI code assistants to deliver code.-Ensure application diagrams and documentation stay current and relevant.-Adherence to agile development methodologies. Key Accountabilities : ·Collaboration with corporate technology teams on architectural designs.·Partner with product teams to assist in roadmap initiatives and sequences.·Participation in Agile ceremonies. Education •Bachelor’s in a relevant field of work or an equivalent combination of education and work-related experience. Experience -Typically, a minimum of 6+ years of software engineering experience, progressive work-related experience with demonstrated proficiency in multiple disciplines, technologies, or processes related to the position.-Proven professional experience building and deploying software in a production environment.-Demonstrated experience in developing and deploying machine learning models or agentic applications.-Strong understanding of the full software development lifecycle, including testing, CI/CD, and monitoring.-Experience working with large datasets and complex data pipelines.-Ability to work effectively in a collaborative, agile team environment.-Experience working with a set of geographically dispersed team and bringing a holistic view of development projects.-An innate curiosity and a portfolio or history that demonstrates a commitment to continually learning new technologies as they evolve. Technical Skill & Knowledge -Deep understanding of modern AI concepts, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows.-Proficiency in Python and extensive experience with its scientific computing and ML/DL libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).-Strong theoretical and practical understanding of machine learning, deep learning, and natural language processing (NLP).-Hands-on experience with at least one major cloud provider (GCP, AWS, Azure) and their associated AI/ML services.-Familiarity with MLOps principles and tools for model versioning, deployment, and monitoring (e.g., Docker, Kubernetes, MLflow).-Experience with both SQL and NoSQL databases, and proficiency with data processing technologies like Spark is a plus.-Solid understanding of microservices architecture, API design (e.g., REST, gRPC), and containerization technologies (e.g., Docker, Kubernetes).-Strong analytical and problem-solving skills-Ability to display effective verbal and written communication skills when explaining complex technical issues to a variety of technical audiences, including clients, vendors, senior management and staff.-Direct experience with a major generative AI platform (e.g., Google Gemini, OpenAI).