Greater London
About Apexon: Apexon brings together distinct core competencies – in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences – to help businesses capitalize on the unlimited opportunities digital offers. Our reputation is built on a comprehensive suite of engineering services, a dedication to solving clients’ toughest technology problems, and a commitment to continuous improvement. Backed by Goldman Sachs Asset Management and Everstone Capital, Apexon now has a global presence. About the Role We are seeking a highly motivated and experienced AI Engineer to join our Data and AI team. This role is ideal for someone who has a strong track record of designing, developing, and deploying AI/ML solutions in a business context—from ideation through to production. You will play a pivotal role in shaping and implementing advanced AI-driven insights and personalization strategies that optimize client engagement and campaign performance across digital, CRM, and relationship management channels for our Financial Services client. Key Responsibilities • End-to-End AI Solution Delivery: Lead AI/ML initiatives from conceptual design to production deployment, including problem scoping, data acquisition, model development, validation, deployment, and monitoring., • AI-Driven Marketing Insights: Develop predictive and generative models to support audience segmentation, personalization, channel optimization, lead scoring, and campaign measurement., • Collaborative Development: Work closely with marketing strategists, data analysts, data engineers, and product owners to define use cases and deliver scalable solutions., • Model Deployment & Monitoring: Deploy models using MLOps practices and tools (e.g., MLflow, Airflow, Docker, cloud platforms) ensuring performance, reliability, and governance compliance., • Innovation & Research: Stay current on advancements in AI/ML and proactively bring forward new ideas, frameworks, and techniques that can be applied to marketing use cases., • Data Strategy: Collaborate with data engineering teams to ensure the availability of clean, structured, and enriched data pipelines required for model training and inference. Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, or a related field., • 4+ years of experience in building and deploying AI/ML models in a business setting, ideally in a regulated or enterprise environment., • Demonstrated experience taking AI solutions from ideation to production—successfully navigating cross-functional stakeholders, data challenges, and deployment hurdles., • Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders., • Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch)., • Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker., • Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention., • Experience working with multi-channel marketing data: CRM (e.g., Salesforce), email, web analytics, social media, and paid media., • Excellent problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks: • Scikit-learn, XGBoost, LightGBM, StatsModels, • PyCaret, Prophet, or custom implementations for time series, • A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : • Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc., • SQL: Advanced querying for large-scale datasets., • Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : • Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery), • Familiarity with data pipelines and orchestration tools like Airflow, • Work closely with Data Engineers to ensure model-ready data and scalable pipelines. Nice to have • Prior experience working in financial services or within a marketing analytics function., • Knowledge of customer lifetime value modelling, recommendation systems, or NLP-based content personalization., • Exposure to regulatory considerations in marketing data usage (e.g., GDPR, data privacy in finance).