AI/ML Engineer | Contract | Financial Services

Overview

We are looking for a highly skilled AI/ML professionals to design and deliver advanced, scalable AI solutions that solve real-world business challenges.

In this role, you will be part of a high-performing, cross-functional team, driving innovation across areas such as Generative AI, Natural Language Processing (NLP), personalization, and predictive analytics. You will play a key role in building end-to-end AI solutions—from problem definition and data modelling to deployment at scale—while contributing to the organization’s broader AI strategy.

Responsibilities

• Design and develop scalable AI/ML solutions across domains such as Generative AI, NLP, recommendation systems, and time series forecasting
• Build end-to-end machine learning pipelines, from data collection and feature engineering to model development, validation, and production deployment
• Research and apply state-of-the-art AI techniques to enhance existing solutions and solve emerging business problems
• Define and implement deployment strategies for production-grade AI systems on cloud platforms
• Drive technical design and architecture discussions, ensuring scalability, reliability, and performance
• Maintain high standards of code quality, testing, and CI/CD integration
• Collaborate closely with product, engineering, and business teams to translate requirements into technical solutions
• Mentor junior team members and contribute to knowledge sharing within the team

Requirements

• Master’s or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field
• Minimum 3 years of hands-on experience building and deploying AI/ML solutions at scale
• Expertise in at least one or more areas: Generative AI, NLP, Deep Learning, Reinforcement Learning, Recommendation Systems, or Time Series Forecasting
• Proficiency in programming language such as Python, Java, Scala, or Julia
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost)
• Experience with ML lifecycle tools and platforms, such as Airflow, Kubeflow, MLflow, or similar
• Familiarity with containerization and orchestration tools (Docker, Kubernetes)
• Knowledge of model serving, observability, and monitoring tools (e.g., Prometheus, Grafana, Elasticsearch)
• Solid understanding of software engineering best practices, including CI/CD and version control
• Experience working in Agile environments with cross-functional teams
• Good analytical thinking, problem-solving skills, and ability to translate business needs into technical solutions
• Excellent communication and stakeholder management skills