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Lead AI Applied ML Engineer

ExperiencedNo visa sponsorship
J.P. Morgan logo

at J.P. Morgan

Bulge Bracket Investment Banks

Posted 7 days ago

No clicks

**Lead AI Applied ML Engineer (Jersey City, NJ) - Drive AI-Driven Financial Crime Monitoring & Build Top-Tier ML Team** Design, build, and scale Next-Gen AI models for detecting AML risks. Lead a team of 4-5 ML Engineers, influencing operational and development standards. Hands-on responsibilities include designing ML pipelines, models, and risk feature data systems using Databricks. Collaborate with cross-functional teams following Agile processes to continuously improve product reliability and outcomes. Requires **8+ years overall, 4+ years ML experience** and expertise in: - Cloud-based applications & JVM-based languages (Python, Java) - ML frameworks (PyTorch, TensorFlow, etc.) - AWS services & Databricks - Agile methodologies & operational ML support - Preferred: AML experience, MLOps, vector databases familiarity

Compensation
Not specified

Currency: Not specified

City
Not specified
Country
United States

Full Job Description

Location: Jersey City, NJ, United States

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. We are building a next generation, AI-driven Global Financial Crimes Strategic Monitoring solutions that detects AML risk, regulatory violations, transactions risk, misconduct, and behavioral anomalies. 

As a Lead MLE on the team, you will design, build and productionize Risk typologies/ features, data pipelines, supervised and unsupervised ML models, and LLM risk explainability that operate at scale across high-volume banking transactions. You will be responsible for leading a team of 4 to 5 ML engineers. You will work at the intersection of Risk modeling, and NLP architectures, inference systems, regulatory explainability and auditability.  This is a hands-on senior role requiring deep expertise in ML operations, LLM integration, scalable ML systems and production grade engineering discipline. This role offers a chance to collaborate with product managers, architects, data science and operational teams, while also engaging in software engineering communities to explore new and emerging technologies.

 

Job responsibilities

  • Lead a small group of ML engineers
  • Lead and influence team with development and operational standards adherence
  • Design, build, collaborate, and operate ML models
  • Design, build and operate LLM solutions
  • Design and build feedback and accuracy measurement techniques for AI solutions
  • Design, build, and operate risk features data pipelines in Databricks
  • 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

 

Required qualifications, capabilities, and skills

  • 8+ years experience in cloud based applications with 4+ years of experience as an MLE
  • Strong foundation in Information Retrieval, Natural Language Processing and 
  • Expert in functional programming and JVM based languages- Python, Java
  • Experience integrating models into cloud scale, microservices based architectures
  • Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers
  • Hands-on experience with AWS services, and Databricks
  • Experience/Exposure to SQL, NoSQL and messaging stacks
  • Excellent verbal & written communication skills and bias for action and ownership in early stage env
  • Operational experience in supporting an enterprise grade ML application in production

 

Preferred qualifications, capabilities, and skills

  • Knowledge of Firm Databricks CDAO platform is good to have
  • Experience with building production-grade ML pipelines, APIs and MLOps frameworks
  • Experience in AML, monitoring and investigations systems is a strong plus
  • Good understanding of data engineering concepts, distributed systems, and scalable architectures
  • Familiarity with vector databases, model serving, and inference optimization is a plus
As a Lead Applied AI ML Engineer on Global Financial Crimes Tech you would be working at the cross section of ML, Cloud and Compliance

Lead AI Applied ML Engineer

Compensation

Not specified

City: Not specified

Country: United States

J.P. Morgan logo
Bulge Bracket Investment Banks

7 days ago

No clicks

at J.P. Morgan

ExperiencedNo visa sponsorship

**Lead AI Applied ML Engineer (Jersey City, NJ) - Drive AI-Driven Financial Crime Monitoring & Build Top-Tier ML Team** Design, build, and scale Next-Gen AI models for detecting AML risks. Lead a team of 4-5 ML Engineers, influencing operational and development standards. Hands-on responsibilities include designing ML pipelines, models, and risk feature data systems using Databricks. Collaborate with cross-functional teams following Agile processes to continuously improve product reliability and outcomes. Requires **8+ years overall, 4+ years ML experience** and expertise in: - Cloud-based applications & JVM-based languages (Python, Java) - ML frameworks (PyTorch, TensorFlow, etc.) - AWS services & Databricks - Agile methodologies & operational ML support - Preferred: AML experience, MLOps, vector databases familiarity

Full Job Description

Location: Jersey City, NJ, United States

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. We are building a next generation, AI-driven Global Financial Crimes Strategic Monitoring solutions that detects AML risk, regulatory violations, transactions risk, misconduct, and behavioral anomalies. 

As a Lead MLE on the team, you will design, build and productionize Risk typologies/ features, data pipelines, supervised and unsupervised ML models, and LLM risk explainability that operate at scale across high-volume banking transactions. You will be responsible for leading a team of 4 to 5 ML engineers. You will work at the intersection of Risk modeling, and NLP architectures, inference systems, regulatory explainability and auditability.  This is a hands-on senior role requiring deep expertise in ML operations, LLM integration, scalable ML systems and production grade engineering discipline. This role offers a chance to collaborate with product managers, architects, data science and operational teams, while also engaging in software engineering communities to explore new and emerging technologies.

 

Job responsibilities

  • Lead a small group of ML engineers
  • Lead and influence team with development and operational standards adherence
  • Design, build, collaborate, and operate ML models
  • Design, build and operate LLM solutions
  • Design and build feedback and accuracy measurement techniques for AI solutions
  • Design, build, and operate risk features data pipelines in Databricks
  • 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

 

Required qualifications, capabilities, and skills

  • 8+ years experience in cloud based applications with 4+ years of experience as an MLE
  • Strong foundation in Information Retrieval, Natural Language Processing and 
  • Expert in functional programming and JVM based languages- Python, Java
  • Experience integrating models into cloud scale, microservices based architectures
  • Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers
  • Hands-on experience with AWS services, and Databricks
  • Experience/Exposure to SQL, NoSQL and messaging stacks
  • Excellent verbal & written communication skills and bias for action and ownership in early stage env
  • Operational experience in supporting an enterprise grade ML application in production

 

Preferred qualifications, capabilities, and skills

  • Knowledge of Firm Databricks CDAO platform is good to have
  • Experience with building production-grade ML pipelines, APIs and MLOps frameworks
  • Experience in AML, monitoring and investigations systems is a strong plus
  • Good understanding of data engineering concepts, distributed systems, and scalable architectures
  • Familiarity with vector databases, model serving, and inference optimization is a plus
As a Lead Applied AI ML Engineer on Global Financial Crimes Tech you would be working at the cross section of ML, Cloud and Compliance