Job description

Job Description Key Responsibilities • Design, develop, and implement scalable and efficient Generative AI solutions across various business use cases. • Contribute to initiatives focused on automation of complex operations such as SAR (Suspicious Activity Reports), KYC reviews, and fraud risk assessments. • Guide and support cross-functional teams through all phases of solution development – from planning and PoC to deployment. • Provide architectural leadership for AI/ML pipelines, ensuring seamless data integration and API connectivity. • Evaluate and recommend best-in-class GenAI tools, frameworks, and methodologies (e.g., OpenAI, Hugging Face, TensorFlow). • Manage project delivery and resource allocation, ensuring alignment with KPIs and business objectives. • Continuously monitor project risks and implement effective mitigation strategies. • Act as a trusted technical advisor, building strong client relationships and offering strategic input on AI initiatives. • Stay informed on the latest trends in Generative AI, cloud-native technologies, and data science techniques. • Identify opportunities for internal process improvements and proactively recommend solutions. • Collaborate with stakeholders to generate hypotheses, build analytical models, and extract actionable insights. • Analyze large, complex data sets to uncover patterns and trends using both supervised and unsupervised learning algorithms. • Perform data wrangling, model tuning, and optimization to enhance performance and accuracy. Required Skills & Qualifications • 5+ years of experience in solution architecture, AI/ML development, and enterprise software engineering. • Hands-on experience with Generative AI platforms and tools: OpenAI APIs, Hugging Face, TensorFlow, etc. • Strong programming skills in Python (preferred), with additional experience in Java or similar languages. • Proficiency with cloud platforms: AWS, Azure, or Google Cloud; experience in microservices architecture is a plus. • Solid understanding of AI/ML pipelines, data integration, statistics, and API development. • Experience with model development, testing, deployment, and performance tuning. • Strong problem-solving and analytical skills. • Excellent communication, presentation, and stakeholder management skills. • Ability to manage multiple priorities in a fast-paced and dynamic work environment.

Requirements