We are hiring a Machine Learning Engineer, Surveillance to build a next-generation, AI-driven platform that detects regulatory violations, insider risk, misconduct, and behavioral anomalies. You will design, build, and productionize ML and LLM powered detection systems that operate at scale across high-volume communication streams.
What You'll Do
- Design LLM powered features such as risk detection, alert explanation, conversation summarization, and reviewer assisted co-pilots.
- Implement explainability techniques ensuring model outputs are traceable, versioned, and reproducible.
- Optimize inference latency and token efficiency for production environments.
- Implement RAG and LLM based risk analysis pipelines processing data at web scale.
- Bake in augmentation mechanisms leveraging legacy regular expressions for filtering and optimization.
- Design real-time and batch processing and scoring pipelines.
- Implement experiment tracking, model versioning and CI/CD for ML.
- Conduct monitoring to detect and alert drift, bias, and performance degradation.
- Work closely within a cross-functional team following agile based processes.
- Collaborate closely with Product Managers, SRE and Compliance SMEs to continuously improve product adoption, reliability, and outcomes.
What We're Looking For
- 8+ years experience in cloud based applications.
- 4+ years of experience as a Machine Learning Engineer.
- Strong foundation in Information Retrieval and Natural Language Processing.
- Expert in functional programming and JVM based languages including Python, Kotlin, and Java.
- Experience integrating models into cloud scale, microservices based architectures.
- Experience with one or more ML frameworks such as PyTorch, Tensorflow, SciKit, NeMo, or Huggingface Transformers.
- Hands-on experience with AWS services such as SageMaker, ECS, Lambda functions, and Bedrock.
- Experience/Exposure to SQL, NoSQL and messaging stacks.
- Excellent verbal & written communication skills and bias for action and ownership in early stage environments.
- Operational experience in supporting an enterprise grade ML application in production.
Nice to Have
- Knowledge of Databricks.
- Experience with MLOps frameworks such as MLflow or Kubeflow.
- Experience in surveillance, fraud detection, fintech or risk systems.
Technical Stack
- Languages: Python, Kotlin, Java
- ML Frameworks: PyTorch, Tensorflow, SciKit, NeMo, Huggingface Transformers
- Cloud & Services: AWS, SageMaker, ECS, Lambda, Bedrock
- Data & Pipeline: SQL, NoSQL, Kafka, Spark
- Tools: MLflow, Kubeflow, Databricks
We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.
