Senior Developer, Technology Delivery – AI Development

Description

This hands-on developer role sits in the Compliance, Surveillance and Supervision area, where you build and support artificial intelligence and machine learning solutions.

You will work alongside senior developers, data scientists, architects and business partners on generative AI applications, intelligent automation, reusable AI services, data pipelines, APIs and integrations with vendor and in-house platforms.

We want someone who pairs solid software engineering with working skill in Python, ML frameworks, large language models, retrieval-augmented generation, prompt engineering, cloud AI services and responsible AI, so that solutions are secure, scalable, explainable and well governed.

The company provides capital markets products and services to corporate, government and institutional clients, with more than 6,500 staff in 40 cities worldwide. It acquired Cowen Inc. in 2023, adding a U.S. equities business.

Responsibilities

• Design, build, test and maintain AI-enabled applications, ML components, intelligent automation, APIs and supporting services.
• Build and improve generative AI features using LLMs, prompt engineering, RAG, embeddings, vector search and agent-based workflows.
• Prepare, transform and check structured and unstructured data for model development, grounding, evaluation and production inference.
• Connect AI solutions to enterprise applications, data platforms, vendor products and cloud services through secure APIs and reusable services.
• Build evaluation methods covering accuracy, relevance, robustness, bias, explainability, latency and cost, and document the results for review.
• Put monitoring, logging, testing, versioning and deployment controls in place across the lifecycle using MLOps and DevSecOps practices.
• Apply privacy, security, responsible AI, model risk, data governance and records management rules in design and delivery.
• Troubleshoot application, data, model and integration problems, and escalate technical risks, limits and dependencies promptly.
• Turn use cases into production-ready solutions with developers, data scientists, quality engineers, analysts, risk partners and product owners.
• Review code, write technical documentation, model cards, implementation guides and development standards, and share knowledge with the team.
• Take part in Agile ceremonies, estimation, sprint planning, proofs of concept and continuous improvement.
• Analyze a broad range of business requirements and research programs and systems to identify the components needed for delivery and issue resolution.
• Build new applications and run unit and component tests against business needs and technology architecture standards.
• Keep stakeholders informed about solution requirements, project plans, estimates, timelines and status, and flag issues that affect other areas of the project.
• Handle small enhancements and break/fix changes, including source code changes, to support business requests.
• Work with business and systems analysts, developers and solution designers so configuration and custom components meet requirements and performance goals.
• Write clean, defect-free code to standard, follow secure coding practices, and run unit and integration tests against design specifications.
• Support testing cycles and post-production deployment, and handle systems administration: performance monitoring, configuration, backup, authentication and tuning.
• Set up and track state-of-health monitoring and monthly SLA targets, and make sure they are met.
• Provide technical input during incidents: review reports and outages, assess impact, and communicate internally and externally until resolution.
• Take part in scope, risk and cost assessments, and help build processes that prevent issues.
• Keep current with new technologies and trends, and build your knowledge of the business, applications, infrastructure and analytical tools.
• Manage your own workload to meet timelines, and suggest ways to improve team productivity and operational efficiency.
• Build working relationships with business and technology partners and program and project managers, and share knowledge within the team and business units.

Requirements

• Strong Python skills and familiarity with common AI and data libraries.
• Hands-on experience with generative AI and LLM concepts such as prompt engineering, embeddings, vector databases, RAG, tool calling and AI agents.
• Working knowledge of AI development frameworks and toolsets.
• Exposure to enterprise AI and model platforms such as Azure AI Foundry, Azure OpenAI Service, Amazon Bedrock, Google Vertex AI or similar technologies.
• Knowledge of RESTful APIs, microservices, Git, SQL, data processing and software development practices.
• Understanding of model evaluation, experimentation, testing, observability and MLOps, including model and prompt versioning and CI/CD automation.
• Awareness of responsible AI principles: fairness, transparency, explainability, privacy, security, human oversight and appropriate use of enterprise data.
• Strong analytical and problem-solving skills, with the ability to assess model outputs, spot failure modes and explain technical limits.
• Clear written and verbal communication and the ability to work well in a cross-functional delivery setting.
• A continuous learning mindset and interest in applying AI to compliance, trade surveillance, supervision and capital markets use cases.
• Demonstrated ability to learn emerging technologies, act on feedback, solve technical problems and communicate with cross-functional teams.
• A bachelor's degree in computer science, software engineering, data science, artificial intelligence, information technology or a related field. Equivalent practical experience may be considered.
• A college degree, specialized training or equivalent work experience.
• Typically 3-5 years of relevant software development, data engineering, ML or AI solution delivery experience.
• Practical experience building applications in Python and integrating models, data services or AI capabilities through APIs.
• Experience with at least one machine learning or generative AI framework and exposure to LLM application patterns.
• Familiarity with Agile delivery methods, cloud technologies, DevOps practices and secure software development (preferred).
• Experience with cloud platforms, containerized applications, automated testing, CI/CD or MLOps practices (preferred).
• Java or Spring Boot experience (asset).
• Experience evaluating model outputs and implementing controls for quality, security, privacy, explainability and responsible AI (asset).
• Financial services, compliance, trade surveillance, market abuse monitoring or capital markets knowledge (asset, not required).

Conditions

Location: New York, New York, United States of America.

Hours: 40.

Line of business: Technology Solutions.

Base pay: $120,000 - $150,000 USD. Actual pay may vary based on skills, experience, job knowledge, geographic location and business needs.

Pay is designed so you can move through the salary range over time as you grow in the role.

You are encouraged to ask compensation questions and talk openly with your recruiter, who can give specific details for this role.

The company aims for a respectful workplace where diverse perspectives are valued and everyone has fair chances to grow.

Benefits

• Base salary plus variable pay, which may include cash and/or equity incentive awards, generally through an incentive plan.
• Health and well-being benefits, plus savings and retirement programs.
• Paid time off, including Vacation PTO, Flex PTO and Holiday PTO.
• Banking benefits and discounts.
• Career development, reward and recognition programs.
• Regular career, development and performance conversations with your manager.
• Access to an online learning platform and a range of mentoring programs.
• Training and onboarding sessions to help you settle into the role.