Mitek Systems

• Execute and optimise regional demand generation programmes across EMEA that drive pipeline for the direct sales organisation. • Deliver integrated campaigns, including: • Webinars • Digital marketing campaigns • Account-based marketing initiatives • Executive roundtables • Sponsored and owned events • Industry conferences • Develop and manage partner marketing programmes that enable existing and new partners to generate demand and accelerate pipeline. • Create and support partner marketing initiatives, including: • Co-marketing campaigns • Partner activations • Enablement resources • Campaign toolkits • Marketing collateral • Joint go-to-market initiatives • Measure, analyse, and optimise campaign performance, pipeline contribution, and marketing ROI. • Provide regular reporting and recommendations to improve programme effectiveness and conversion across the funnel. • Collaborate closely with EMEA Sales, Channel, Business Development, and Global Marketing teams to align marketing plans with regional priorities. • Coordinate campaign execution across internal teams and external partners to maximise business impact. • Manage external agencies, event partners, and marketing suppliers to ensure programmes are delivered on time, within budget, and to a high standard.

• Review identity documents for administrative and legal accuracy • Quality-assess images captured by the customer • Assess whether the document is authentic/fraudulent • Check the accuracy of information already in the system • Extract document data and input into the system • Classify document types • Identify key document security features

• Design, build, and deploy AI solutions powered by ML, LLMs, and agentic AI systems that solve real business problems. • Define evaluation strategies upfront for each use case, including task success metrics, offline and online evaluation plans, error analysis, and production monitoring requirements. • Build and improve LLM-based systems using prompt engineering, retrieval-augmented generation (RAG), context engineering, and multi-step agentic workflows. • Partner closely with product, engineering, data, and business stakeholders to prioritize AI use cases and align on success metrics, operational requirements, and delivery timelines. • Apply strong production practices across AI systems, including experimentation, versioning, observability, alerting and continuous improvement in production • Monitor, troubleshoot, and improve production AI systems by balancing quality, latency, cost, reliability, and maintainability.

• Build, train, and optimize computer vision models for image classification, face liveness detection, and presentation attack detection (PAD) / anti-spoofing • Work on real-world identity verification and biometric authentication problems, improving model performance on noisy, adversarial inputs such as spoofed images, replay attacks, deepfakes, and synthetic media • Design and run experiments to improve model accuracy, recall, robustness, and fraud detection performance using techniques such as augmentation, class balancing, architecture tuning, and hard-negative mining • Design, train, and improve deep learning models (e.g., CNNs, Vision Transformers, and foundation models), including loss function design, hyperparameter optimization, and performance tuning on large-scale image datasets • Prepare and curate large, noisy datasets, including data ingestion, validation, cleaning, deduplication, labeling strategies, and dataset QA to improve model reliability and generalization • Develop evaluation protocols and success metrics that balance fraud detection effectiveness, false acceptance rates, false rejection rates, and overall business impact • Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, observability, and cost controls • Productionize models as resilient Python services and libraries; collaborate with platform teams to optimize APIs, latency, scalability, and operational reliability • Contribute to the evolution of our Identity Verification (IDV) platform by modernizing legacy components and improving model performance, maintainability, and modularity • Partner closely with Product, Customer Success, Fraud, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, security, and reliability requirements • Support and mentor other engineers through design reviews, code reviews, experimentation best practices, and knowledge sharing • Research and evaluate emerging techniques in face liveness detection, presentation attack detection (PAD), deepfake detection, biometric authentication, and adversarial machine learning to strengthen our fraud prevention capabilities

• Design, develop, deploy, and maintain production-ready, scalable API services • Build platform solutions using microservices and containerized architectures • Write and maintain unit, integration, and performance tests • Develop and maintain internal and public API documentation • Collaborate with engineering, product, and operations teams across the full development lifecycle • Participate in Agile/Scrum ceremonies (stand-ups, planning, refinement, retrospectives) • Support deployment, monitoring, and operational readiness of services in production

• Evolve and improve software for products and services • Work on products and solutions that will help to transform mobile banking and the identity authentication markets

• Drive data infrastructure that enables data-informed decision-making • Partner closely with Product Managers, Data Analysts, Software Engineers, and business stakeholders • Deliver stable, high-quality data pipelines, enterprise reporting, and datasets • Optimize and maintain OLTP databases, ensuring reliability, performance, and production readiness • Audit systems, improve data quality, refactor legacy pipelines, and apply best practices • Build and maintain scalable data systems that power advanced analytics across the business • Design performance testing and release validation frameworks to prevent regressions • Establish strong production processes such as monitoring, alerting, backup and recovery, access controls, and incident response

• Design, build, and deploy production-grade APIs and services within a microservices/serverless architecture. • Own your code from design to deployment, ensuring scalability, security, and performance at every step. • Automate testing and delivery through robust unit, integration, and performance tests for reliability at scale. • Document your work clearly for internal stakeholders and external developers consuming our public APIs. • Collaborate cross-functionally with product managers, researchers, and operations teams to translate ideas into production-ready features. • Participate in Agile ceremonies (stand-ups, sprint planning, retrospectives) and help drive continuous improvement. • Develop clean, maintainable, and production hardened code.

• Design, develop, test, and release production-ready mobile web SDKs, demo apps and code samples. • Integrate 3rd-party libraries and internal C++ computer vision libraries into the SDKs. • Write and execute automated unit/functional/integration/UI tests to ensure the quality of our products. • Create and maintain internal software documentation and public Developer’s Guide. • Collaborate with other software engineers, researchers, product managers, and cloud/operation engineers to determine requirements and ensure alignments. • Follow Agile/Scrum processes like daily stand-ups, refinements, planning, and retrospectives.

• Design, build, and deploy AI solutions powered by ML, LLMs, and agentic AI systems that address clear business problems. • Define evaluation strategies upfront for each use case, including task success metrics, offline and online evaluation plans, error analysis, and production monitoring requirements. • Build and improve LLM-based systems using prompt engineering, retrieval-augmented generation, and multi-step workflows. • Apply MLOps and LLMOps practices, including experimentation, versioning, observability, alerting, model and prompt evaluation, and continuous improvement in production. • Partner closely with product, engineering, and business stakeholders to prioritize AI use cases and align on success metrics, operational needs, and delivery timelines.