Designs, develops, trains, and deploys artificial intelligence systems and machine learning models to solve real-world problems and automate tasks. Responsibilities include selecting and optimizing AI models, building data pipelines, writing robust code, integrating AI solutions into applications, and collaborating with cross-functional teams to align AI initiatives with business goals. Utilizes tools such as Python and frameworks such as TensorFlow and PyTorch, focusing on areas like natural language processing (NLP), computer vision, and predictive analytics to create scalable and efficient AI-powered applications.
RESPONSIBILITIES:
AI System Design and Development
- Creates AI systems and algorithms using machine learning, deep learning, and reinforcement learning to automate tasks and decision-making
Model Building and Training
- Develops, trains and validates machine learning models using large datasets and frameworks like TensorFlow and PyTorch
Data Engineering and Management
- Collects, cleans, and manages large datasets to train and improve AI models, ensuring data quality and accessibility
Performance Optimization
- Tunes and optimizes AI models for efficiency, accuracy, and performance using statistical analysis
Deployment and Integration
- Deploys AI models into production environments via APIs and microservices and integrate them into existing business processes
Research and Innovation
- Stays updated on advancements in AI, exploring new techniques and technologies to drive innovation and address complex challenges
Testing and Validation
- Tests and validates AI models to ensure reliability, accuracy, and ethical compliance before full deployment
Collaboration
- Works with data scientists, software engineers, product managers, and stakeholders to ensure AI solutions meet business objectives
QUALIFICATIONS:
- Must be a Bachelor's degree in Computer Science, Engineering, Data Science, or related fields
- Must have at least 5 years of experience designing and implementing AI-driven solutions in enterprise environments
- Must be a Certified Azure AI Practitioner or AWS AI Engineer with demonstrated cloud architecture expertise
- Must have strong background in generative AI solutions and large language model architecture
- Must have proven experience in data analytics and visualization platform architecture design
- Must have strong data engineering skills with expertise in capacity planning and load management
- Must have advanced proficiency in programming languages like Python
- Must have extensive, hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras
- Must have experience working with big data tools like Apache Spark, Hadoop, and Kafka to process and manage large-scale datasets
- Must have experience with major cloud providers (e.g., AWS, Google Cloud, Azure) and their AI/ML services for building and deploying scalable systems
- Must have knowledge of software engineering best practices, including containerization (Docker), orchestration (Kubernetes), and managing the AI/ML lifecycle in production
- Preferably has experience in banking or financial services industry with understanding of regulatory architecture requirements
- Preferably has background in MLOps architecture and AI platform governance frameworks
