About the Company
Priorise (https://priorise.co/) provides end-to-end Decision Science and Project Management solutions by combining business thinking with AI- and ML-powered technologies.
We work closely with our clients to understand their business context and deliver practical, implementation-focused solutions. We value ownership, curiosity, and resilience, and we’re deeply committed to solving real client problems.
About the Role
We’re seeking a Senior Data & AI Engineer who bridges the worlds of data science, AI engineering, and data platform architecture. This isn’t a narrow developer role—it’s ideal for someone who can think like a consultant, operate like a product owner, and deliver like an engineer.
You’ll design end-to-end AI-native data systems, build robust ML and data pipelines, and translate business challenges into intelligent, measurable outcomes.
Key Responsibilities
- Design, develop, and maintain end-to-end data and AI pipelines spanning ingestion, processing, modeling, and consumption.
- Architect scalable data platforms across batch and streaming workflows using Python, SQL, and PySpark.
- Build data science and ML solutions — from exploratory analysis and feature engineering to model training, evaluation, and deployment.
- Implement advanced analytics and applied ML techniques (predictive modeling, time-series forecasting, classification, optimization, etc.).
- Orchestrate pipelines using Airflow or DAG-based systems, ensuring reliability through robust scheduling, retries, and monitoring.
- Deploy ML models via MLOps best practices, integrating with APIs or microservices for real-time decisions.
- Conduct data validation, quality assurance, and performance optimization across datasets and workflows.
- Collaborate with product managers and clients to frame problems, define KPIs, and identify AI opportunities.
- Build dashboards or feature stores that enable insight-driven decision-making and ML experimentation.
- Act as a consultant and thought partner, not just an executor—leading design sessions, guiding data strategy, and ensuring business alignment.
- Optimize compute and storage costs across cloud data ecosystems (AWS, GCP, Azure).
- Develop reusable patterns, accelerators, and frameworks for faster AI and data model development.
- Lead and mentor junior engineers in ML pipeline design, data modeling standards, and best practices.
Required Skills & Experience
- 5+ years of experience spanning data science, AI/ML engineering, and data platform development.
- Hands-on expertise across Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL/PySpark.
- Strong grasp of feature engineering, model evaluation, and statistical analysis.
- Experience building data pipelines and ML workflows using orchestration tools like Airflow, Dagster, or Prefect.
- Proven experience with cloud data stacks (Snowflake, Databricks, BigQuery, Redshift, etc.).
- Familiarity with MLOps, containerization (Docker), and version control (Git, CI/CD).
- Strong understanding of ETL/ELT design, data modeling, and data quality frameworks.
- Ability to communicate technical findings clearly to non-technical stakeholders.
- Ownership mindset – thrives in ambiguous, outcome-driven environments.
- Product thinking – ability to connect data capabilities with desired business impact.
What You Will Be Getting
- A flexible work environment, with opportunities to manage your time and productivity.
- Constant learning from a multi-disciplinary team and global client exposure.
- A steep growth curve, with real ownership and the ability to create a visible impact.
- An open, collaborative team culture that values innovation and accountability.
Other Requirements
- Willingness to work in IST & PST time zone.
- Flexibility in work schedule can be discussed further once work begins.
Interested?
If you’re excited by this opportunity and meet the criteria above, please acknowledge by sharing your updated CV with us.
