The AI Engineer will be responsible for design development of innovative AI solutions and contributing to the strategic vision of our organization.
Requirements
- Python
- AI: Machine learning, deep learning, neural networks, NLP, LLM’s
- Containers (Azure Kubernetes)
- CI/CD (Azure DevOps, Git, SonarQube)
- Cloud Platform (Microsoft Azure)
- Experience with LLMs, transformers, and understanding of agentic systems, current limitations and opportunities.
- Proficiency in programming languages such as Python and proficiency with AI frameworks (e.g., TensorFlow, PyTorch)
Responsibilities
- Define and implement GenAI building standards to ensure consistency, quality, and scalability across projects
- Stay abreast of advancements in GenAI and agentic systems, translating emerging trends into actionable opportunities
- Hands-on engineer in technical design and development of AI applications using existing and/or emerging technology.
- Work closely with product managers, data scientists and other stakeholders to translate business requirements into scalable and efficient AI solutions.
- Collaborate with cross-functional teams to execute the AI technology roadmap, aligning with business objectives and industry trends.
- Stay abreast of the latest advancements in AI technologies, guiding the team in adopting best practices and cutting-edge methodologies.
- Responsible for development of AI specific software/platform components.
Other
- 5+ years in development using multiple technology stack capabilities (Front/Back End, API, Database Mgmt., Hosting technologies etc. to drive digital transformation in financial services or technology domain
- Proven expertise in developing and delivering GenAI applications, including agentic systems
- Strong understanding of modern AI/ML frameworks, cloud platforms (e.g., AWS, Azure, GCP), and data engineering practices.
- Experience in implementing Enterprise AI solutions leveraging AIML models.
- Ways of Working – Adoption of the Agile ways of working in the software delivery lifecycle and continuous learning of new methods and technology
