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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:

• 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).