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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).
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