AVP – Model Ops Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Java Backend Engineer

Responsibilities: <ul><li>Design, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).</li><li>Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.</li><li>Develop reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that support various model types and regulatory needs.</li><li>Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking.</li><li>Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance).</li><li>Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.</li><li>Work closely with the model monitoring analytics and strategy monitoring analytics teams within MO&amp;A to ensure the monitoring data infrastructure adapts to changing analytics and monitoring needs. Enable visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that summarize model health, stability, and issue alerts.</li><li>Designing and maintaining high-performance data pipelines that ingest, transform, and version datasets for Model and Strategy Monitoring</li><li>Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles.</li></ul> Requirements: <ul><li>Bachelor’s degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 5+ years’ experience or in lieu of degree, and 7+ years of relevant work experience in, data engineering or related roles in the financial services or regulated analytics domain.</li><li>Strong proficiency with data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark).</li><li>Proficient in programming languages such as SAS, Python, and SQL for building monitoring pipelines and validation checks.</li><li>Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing (e.g., Snowflake, Redshift, BigQuery).</li><li>Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, Why Labs, Prometheus).</li><li>Understanding of model risk governance requirements and the role of data engineering in ensuring compliant model monitoring.</li><li>Ability to work in an agile environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.</li></ul>

Responsibilities: Architecture &amp; Design:&nbsp; <ul><li>Design end-to-end data architectures that integrate real-time streaming (Kafka, GCP Pub/sub) and batch processing (Big Query OR Databricks).</li></ul> Pipeline Development:&nbsp; <ul><li>Build and maintain robust ETL/ELT pipelines flowing into BigQuery/Databricks, ensuring data is clean, modeled, and ready for consumption.</li></ul> Custom Integration:&nbsp; <ul><li>Develop custom APIs and microservices using Java/Python/C# to facilitate data movement between niche systems and the data lakehouse.</li></ul> Infrastructure Management:&nbsp; <ul><li>Deploy and manage data workloads on Google Kubernetes Engine (GKE), ensuring high availability and cost-efficiency.</li></ul> Technical Leadership:&nbsp; <ul><li>Mentor junior engineers, conduct code reviews, and establish DataOps best practices (CI/CD, automated testing, and monitoring).</li></ul> Data Reliability:&nbsp; <ul><li>Implement data quality frameworks to ensure accuracy and lineage across the hybrid environment.</li></ul> Requirements: <ul><li>Experience with Terraform &amp; Azure Dev Ops (ADO).</li><li>Knowledge of workflow orchestrators like Apache Airflow (Cloud Composer).</li><li>Experience implementing Data Governance (metadata management, data cataloging).</li><li>Data Processing &amp; Storage</li><li>BigQuery: Expert-level SQL and experience with partition/cluster optimization.</li><li>Databricks: Deep knowledge of the Lakehouse architecture, Delta Lake, and Spark (PySpark/Scala) Software Engineering &amp; APIs</li><li>Languages: Proficiency in Java/Python/C# for backend development and custom data connectors.</li><li>API Design: Proven ability to build and consume RESTful APIs for data ingestion Streaming &amp; Infrastructure</li><li>Kafka/GCP Pub-Sub: Experience building event-driven architectures and managing producers/consumers.</li><li>GKE: Proficiency in containerization (Docker) and orchestrating data workloads on Kubernetes.</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: <ul><li>Collaborate with team, high availability, concurrent and fault tolerant software systems.</li><li>Significantly contribute to development of Aladdin’s global, multi-asset trading platform.</li><li>Work with product management and business users to define the roadmap for the product.</li><li>Design and develop innovative solutions to complex problems, identifying issues and roadblocks.</li><li>Apply validated quality software engineering practices through all phases of development.</li><li>Ensure resilience and stability through quality code reviews, unit, regression and user acceptance testing, dev ops and level two production support.</li><li>Be a leader with vision and a partner in brainstorming solutions for team productivity, efficiency, guiding and motivating others.</li><li>Drive a strong culture by bringing principles of inclusion and diversity to the team and setting the tone through specific recruiting, management actions and employee engagement.</li><li>For VP Level: In addition to above, a VP level candidate should be able to lead individual projects priorities, deadlines and deliverables using AGILE methodologies.</li></ul> Requirements: Qualifications: <ul><li>B.S. / M.S. degree in Computer Science, Engineering, or a related subject area</li><li>For VP Level: 8+ years of proven experience</li><li>For Associate Level: 4+ years of experience</li></ul> Skills and Experience: <ul><li>A proven foundation in core Java and related technologies, with OO skills and design patterns</li><li>Track record building high quality software with design-focused and test-driven approaches</li><li>Good hands-on object-oriented programming knowledge in Java, Spring, TypeScript, JavaScript, Microservices, Angular (2.0+), React.</li><li>Strong knowledge of Open-Source technology stack (Spring, Hibernate, Maven, JUnit, etc.).</li><li>Experience with relational database and/or NoSQL Database (e.g., Apache Cassandra)</li><li>Great analytical, problem-solving and communication skills</li><li>Some experience or a real interest in finance, investment processes, and/or an ability to translate business problems into technical solutions.</li><li>For VP Level: In addition to the above, a VP level candidate should have experience leading development teams, projects or being responsible for the design and technical quality of a significant application, system, or component. Ability to form positive relationships with partnering teams, sponsors, and user groups.</li></ul> Nice to have and opportunities to learn: <ul><li>Exposure to building microservices and APIs ideally with REST, Kafka or gRPC.</li><li>Experience working in an agile development team or on open-source development projects.</li><li>Experience with optimization, algorithms or related quantitative processes.</li><li>Exposure to high scale distributed technology like Kafka, Mongo, Ignite, Redis</li><li>Experience with Cloud platforms like Microsoft Azure, AWS, Google Cloud</li><li>Experience with DevOps and tools like Azure DevOps</li><li>Experience with AI-related projects/products or experience working in an AI research environment.</li><li>A degree, certifications or opensource track record that shows you have a mastery of software engineering principles.</li></ul>

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AVP – Model Ops Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Java Backend Engineer

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AVP – Model Ops Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Java Backend Engineer

WhiteCrow WhiteCrow is on a mission to connect companies with the best talent across the globe.

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