Company Overview
Our client is a globally regulated financial technology and online trading organization serving clients across multiple international markets. With a strong focus on innovation, digital transformation, and emerging technologies, the organization is investing heavily in Artificial Intelligence, Machine Learning, and intelligent automation to enhance operational efficiency, client experiences, and next-generation financial products. As part of its continued growth, the company is seeking experienced AI professionals to build and deploy advanced AI solutions across a complex, high-scale financial ecosystem.
Role Overvie
wWe are seeking a highly skilled Senior AI Engineer to lead the development and deployment of advanced Artificial Intelligence, Machine Learning, Large Language Model (LLM), and Agentic AI solutions. This role will be responsible for designing, building, and scaling production-grade AI systems that solve complex business challenges across financial services, trading operations, customer experience, knowledge management, automation, and intelligent decision-making
.The successful candidate will combine strong machine learning expertise with hands-on experience in Generative AI, Retrieval-Augmented Generation (RAG), Agentic Frameworks, and MLOps practices to deliver innovative solutions from concept through production deployment
.
Responsibiliti
- esDesign, develop, test, and deploy advanced machine learning and deep learning models addressing classification, prediction, anomaly detection, recommendation, segmentation, and forecasting use case
- s.Build and optimize Large Language Model (LLM) applications leveraging technologies such as GPT, Claude, Llama, Falcon, and other foundation model
- s.Design and implement Retrieval-Augmented Generation (RAG) architectures, vector databases, semantic search solutions, and knowledge retrieval system
- s.Develop Agentic AI solutions including autonomous agents, workflow orchestrators, multi-agent systems, tool-using agents, and AI-powered automation framework
- s.Lead the end-to-end machine learning lifecycle including data preparation, feature engineering, model training, evaluation, deployment, monitoring, and continuous optimizatio
- n.Collaborate with Product, Engineering, Data, Operations, Compliance, and Business teams to identify AI opportunities and translate business requirements into scalable AI solution
- s.Implement MLOps best practices including CI/CD pipelines, model versioning, experiment tracking, monitoring, governance, and reproducibility standard
- s.Build multimodal AI applications capable of processing text, documents, images, structured data, and other business information source
- s.Monitor production AI systems for performance degradation, model drift, data quality issues, and operational reliabilit
- y.Conduct AI experimentation and rapid prototyping to evaluate emerging technologies and assess their business valu
- e.Mentor junior AI and Data Science professionals while contributing to internal knowledge-sharing initiative
- s.Stay current with advancements in Artificial Intelligence, Machine Learning, Generative AI, Agentic AI, and emerging technologies, identifying opportunities for adoption across the organizatio
- n.Contribute to the organization's long-term AI strategy, architecture, and innovation roadma
p.
Requireme
- ntsBachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Applied Mathematics, Statistics, or a related discipli
- ne.5+ years of hands-on experience developing and deploying Machine Learning and Deep Learning solutions in production environmen
- ts.Proven experience building end-to-end AI and Machine Learning systems from concept through deployment and optimizati
- on.Strong expertise with Python and modern AI development ecosyste
- ms.Advanced experience with machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, and related technologi
- es.Demonstrated experience building and deploying LLM-based applications including fine-tuning, prompt engineering, RAG implementations, and model evaluati
- on.Hands-on experience developing Agentic AI solutions using frameworks such as LangGraph, AutoGen, CrewAI, LangChain, LlamaIndex, or similar technologi
- es.Experience integrating AI solutions into enterprise applications and production environmen
- ts.Strong understanding of Transformers, CNNs, Diffusion Models, Graph Neural Networks, and modern AI architectur
- es.Experience working with OpenAI, Anthropic, Hugging Face, and related AI platforms and AP
- Is.Familiarity with Docker, Git, CI/CD pipelines, experiment tracking, and MLOps tooli
- ng.Strong analytical, problem-solving, and communication skil
- ls.Ability to operate independently while collaborating effectively within cross-functional tea
- ms.Previous experience within Financial Services, FinTech, Capital Markets, Trading, Brokerage, Payments, or Digital Assets environments is highly desirab
le.
