Job
Description
Role Summary: The Technical Lead/Manager will be responsible for leading the AI CoE team, driving the implementation of AI-powered solutions for the organization. They will coordinate across multiple teams, work closely with stakeholders, and oversee the technical aspects of AI/ML projects, ensuring the effective application of generative AI technologies to the product suite. Key Responsibilities: Lead the AI CoE team, ensuring alignment with business objectives. Oversee the design, development, and deployment of AI/ML models for financial use cases. Work closely with product managers and business stakeholders to understand requirements and deliver actionable AI solutions. Provide guidance and mentorship to junior team members. Manage project timelines, resources, and risk assessments. Collaborate with Data Engineers, AI/ML Engineers, Full Stack Engineers, and QA Engineers to ensure smooth integration of AI solutions. Stay updated with advancements in AI/ML technologies and best practices. Required Experience: 8-12 years of experience in AI/ML technologies, with at least 3 years in a leadership role. Strong background in AI/ML model development and deployment, particularly in the financial services sector. Proficient in Python, R, and other AI-related programming languages. Experience managing cross-functional teams in an Agile environment. Strong understanding of data science, cloud computing, and AI/ML frameworks (e.g., TensorFlow, PyTorch, etc.). Tech Specifics: Programming Languages: Expert-level proficiency in Python (e.g., NumPy, Pandas) and R for AI/ML development; familiarity with Scala is a plus. AI/ML Frameworks: Hands-on experience with TensorFlow, PyTorch, and Scikit-learn for building and optimizing models; exposure to Hugging Face for NLP/Generative AI tasks. MLOps Tools: Strong command of Docker and Kubernetes for containerization, and Jenkins or GitHub Actions for CI/CD pipelines. Cloud Platforms: Deep expertise in AWS (e.g., SageMaker, Lambda), Azure (e.g., Machine Learning Studio), or GCP (e.g., AI Platform, BigQuery) for scalable model deployment. Big Data Technologies: Working knowledge of Apache Spark or Hadoop for processing large financial datasets. Database Skills: Proficiency in SQL and experience with NoSQL databases like MongoDB or Cassandra for data integration. Generative AI: Practical experience with models like GPT, LLaMA, or BERT for applications such as automated reporting or customer chatbots.
