Lead Software Engineer Agentic AI Solutions , Java or Python

• Develops secure, high-quality production code for data-intensive applications

requirements through deployment and operational stability

• Drives technical decisions that influence application design, functionality,

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+

• Experience with LLM orchestration frameworks . Hands-on experience with model

• Leads end-to-end design and implementation of complex software features, from

• Builds and maintains agentic AI systems, including multi-agent workflows,

tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments

• Implements LLM-based applications including RAG pipelines, embedding

• Owns observability, evaluation, and safety of production AI systems —

including prompt monitoring, output validation, cost tracking, and latency optimization

• Identifies and executes opportunities to automate remediation of recurring

• Executes creative software solutions, including design, development, and

technical troubleshooting to solve complex and ambiguous problems. Mentors and coaches junior and mid-level engineers, conducting code reviews and sharing engineering best practices

• Contributes to firmwide frameworks, tools, and SDLC practices as an engaged

• Drives adoption and governance of approved AI-assisted engineering practices

across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.

• Applies knowledge of tools within the Software Development Life Cycle

toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

• Hands-on experience building and shipping LLM-based applications and agentic

systems with tool use, memory, and multi-step reasoning in production environments

• Advanced proficiency in one or more programming languages, particularly

• Deep experience with large-scale data processing, microservices, API design,

• Working knowledge of relational and NoSQL databases, vector stores, and data

• Experience with caching technologies (Redis, MemCached), observability tools

(Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)

• Proficiency in CI/CD, test-driven development, automation, and all aspects of

• Strong understanding of agile methodologies, application resiliency, and

• Practical cloud-native engineering experience (AWS, Azure, or GCP)
• Demonstrated experience leading effective use of enterprise-authorized

AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security

• Strong understanding of responsible AI use in engineering workflows,

including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.

• Familiarity with AI evaluation and observability practices: evals frameworks,

red-teaming, prompt drift detection, and cost/latency monitoring.Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows

• Experience with modern data platforms such as Databricks or

Snowflake.Hands-on experience with Spark/PySpark and big data processing at scale

• Knowledge of the financial services industry and its technology system.