Research Data Engineer

Position Overview Job Title: Software Development Engineer 2 Department: Technology Location: Bangalore, India Reporting To: Senior Research Manager - Data Position Purpos e The Research Engineer – Data will play a pivotal role in advancing TookiTaki’s AI-driven compliance and financial crime prevention platforms through applied research, experimentation, and data innovation. This role is ideal for professionals who thrive at the intersection of research and engineering, turning cutting-edge data science concepts into production-ready capabilities that enhance TookiTaki’s competitive edge in fraud prevention, AML compliance, and data intelligence. The role exists to bridge research and engineering by Designing and executing experiments on large, complex datasets Prototyping new data-driven algorithms for financial crime detection and compliance automation. Collaborating across product, data science, and engineering teams to transition research outcomes into scalable, real-world solutions. Ensuring the robustness, fairness, and explainability of AI models within TookiTaki’s compliance platform. Key Responsibilities. Applied Research & Prototyping. Conduct literature reviews and competitive analysis to identify innovative approaches for data processing, analytics, and model developments. Build experimental frameworks to test hypotheses using real-world financial datase Prototype algorithms in areas such as anomaly detection, graph-based analytics, and natural language processing for compliance workflows. Data Engineering for Research Develop data ingestion, transformation, and exploration pipelines to support experimentation. Work with structured, semi-structured, and unstructured datasets at scale. Ensure reproducibility and traceability of experiments Algorithm Evaluation & Optimization. Evaluate research prototypes using statistical, ML, and domain-specific metrics. Optimize algorithms for accuracy, latency, and scalability. Conduct robustness, fairness, and bias evaluations on mode. Collaboration & Integration Partner with data scientists to transition validated research outcomes into production-ready to code. Work closely with product managers to align research priorities with business goals Collaborate with cloud engineering teams to deploy research pipelines in hybrid environments. Documentation & Knowledge Sharing Document experimental designs, results, and lessons learned Share best practices across engineering and data science teams to accelerate innovation Qualifications and Skills EducationRequired: Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or related field Preferred: Master’s or PhD in Machine Learning, Data Engineering, or a related research intensive field Experience Minimum 4–7 years in data-centric engineering or applied research roles. Proven track record of developing and validating algorithms for large-scale data processing or machine learning applications. Experience in financial services, compliance, or fraud detection is a strong plus. Technical Expertise. Progra mming: Proficiency in Scala, Java, or Python Data Proce ssing: Experience with Spark, Hadoop, and Flink ML/Research Frame works: Hands-on with TensorFlow, PyTorch, or Scikit-learn Data bases: Experience with both relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra, ElasticSearch). Cloud Plat forms: Experience with AWS (preferred) or GCP for research and data pipelines. Tools: Familiarity with experiment tracking tools like MLflow or Weights & Biases. Application Deploy ment: Strong experience with CI/CD practices, Containerized Deployments through Kubernetes, Docker Etc. Streaming framew orks: Strong experience in creating highly performant and scalable real time streaming applications with Kafka at the core Data Lakeh ouse: Experience with one of the modern data lakehouse platforms/formats such as Apache Hudi, Iceberg, Paimon is a very strong Plus. Soft Skils SkillsStrong analytical and problem-solving abilities. Clear concise communication skills for cross-functional collaboration. Adaptability in fast-paced, evolving environments. Curiosity-driven with a bias towards experimentation and iteration. Key Competencies Innovation Mindset: Ability to explore and test novel approaches that push boundaries in data analytics. Collab oration: Works effectively with researchers, engineers, and business stakeholders. Technical Depth: Strong grasp of advanced algorithms and data engineering principles. Problem Solving: Dives deep into the logs, metrics and code and identifying problems opportunities for performance tuning and optimization. Own ership: Drives research projects from concept to prototype to production. Adapta bility: Thrives in ambiguity and rapidly changing priorities. Preferred Certifications in AWS Big Data, Apache Spark, or similar technologies. Experience in compliance or financial services domains. Success Metrics Research to Production Co nversion: % of validated research projects integrated into TookiTaki’s platform Model Performan ce Gains: Documented improvements in accuracy, speed, or robustness from research initiatives. Efficiency of Research P ipelines: Reduced time from ideation to prototype completion. Collaboratio n Impact: Positive feedback from cross-functional teams on research integration. Benefits Competiti ve Salary: Aligned with industry standards and experience. Professional De velopment: Access to training in big data, cloud computing, and data integration tools. Comprehensive Benefits: Health insurance and flexible working options. Growth Oppo rtunities: Career progression within Tookitaki’s rapidly expanding Services Delive ry team. Introducing: TookitakiTookitaki: The Trust Layer for Financial Services Tookitaki is transforming financial services by building a robust trust layer that focuses on two crucial pillars: preventing fraud to build consumer trust and combating money laundering to secure institutional trust. Our trust layer leverages collaborative intelligence and a federated AI approach, delivering powerful, AI-driven solutions for real-time fraud detection and AML (Anti-Money Laundering) compliance.How We Build Trust: Our Unique Value Propositions AFC Ecosystem – Community-Driven Financial Crime Protection The Anti-Financial Crime (AFC) Ecosystem is a community-driven platform that continuously updates financial crime patterns with real-time intelligence from industry experts. This enables our clients to stay ahead of the latest money laundering and fraud tactics. Leading digital banks and payment platforms rely on Tookitaki to protect them against evolving financial crime threats. By joining this ecosystem, institutions benefit from the collective intelligence of top industry players, ensuring robust protection.FinCense – End-to-End Compliance. PlatformOur FinCense platform is a comprehensive compliance solution that covers all aspects of AML and fraud prevention—from name screening and customer due diligence (CDD) to transaction monitoring and fraud detection. This ensures financial institutions not only meet regulatory requirements but also mitigate risks of non-compliance, providing the peace of mind they need as they scale. Industry Recognition and Global Impact Tookitaki’s innovative approach has been recognized by some of the leading financial entities in Asia. We have also earned accolades from key industry bodies such as FATF and received prestigious awards like the World Economic Forum Technology Pioneer, Forbes Asia 100 to Watch, and Chartis RiskTech100.Serving some of the world’s most prominent banks and fintech companies, Tookitaki is continuously redefining the standards of financial crime detection and prevention, creating a safer and more trustworthy financial ecosystem for everyone.

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