Job DescriptionAbout the RoleWe're searching for an experienced AI professional to take a leading role in shaping the future of AI within a global enterprise technology environment. As a Lead AI Systems Engineer, you'll contribute to the design and deployment of advanced language models and AI platforms that support next-generation banking, security, and support systems.
This role focuses on building scalable, integrated AI solutions using state-of-the-art natural language processing and machine learning techniques. You'll work on implementing and evolving the company's proprietary AI models, including data acquisition, model tuning, and production-grade deployment - with an emphasis on lightweight, edge-optimized solutions and federated learning. If you're excited by the challenge of deploying impactful AI at scale, we want to hear from you.
Responsibilities- Architect and deploy advanced machine learning models and NLP-driven AI systems that improve product experiences, infrastructure, and core business workflows.
- Collaborate cross-functionally with internal teams and strategic partners (including Microsoft and Intel) to translate technical requirements into functional AI capabilities.
- Develop and fine-tune a lightweight, custom model based on foundational LLM architectures.
- Design systems optimized for edge computing environments with secure, federated learning patterns.
- Drive responsible AI development, ensuring adherence to ethical frameworks and regulatory compliance.
- Support the full AI lifecycle: training, evaluation, deployment, monitoring, and performance optimization.
- Stay on the forefront of industry trends and emerging AI research relevant to enterprise applications.
Requirements- Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
- 5+ years of experience building and scaling AI/ML models in production environments.
- Deep knowledge of NLP, language models, and model tuning (e.g., GPT, BERT, LLaMA, Phi, etc.).
- Proficiency in Python, Java, or R with strong coding fundamentals.
- Experience working with microservices-based systems, RESTful APIs, and containerized workloads.
- Hands-on with cloud platforms (Azure, GCP, or AWS), especially with deploying AI in distributed settings.
- Familiarity with TensorFlow, PyTorch, scikit-learn, and associated data science toolkits.
- Competence in Git, CI/CD, and agile workflows for rapid development cycles.
- Strong analytical and troubleshooting skills with a mindset for innovation and experimentation.
Additional Requirements- Certifications in cloud-based AI tools (e.g., Azure AI Engineer Associate, AWS Machine Learning, Google Cloud ML Engineer).
- Background in enterprise application integration using AI/LLMs.
- Understanding of conversational AI, computer vision, and multimodal learning.
- Practical knowledge of AI governance, compliance, and model accountability frameworks.
- A portfolio or examples of prior work deploying AI in real-world use cases.
