Software Engineer - Senior

As a Software Engineer - Senior, you partner with business partners to turn ambiguous problems into working AI solutions that improve our investment process and business outcomes. You are the build-and-deploy bridge between the people who own the problem and the AI platform that powers the answer: you lead discovery, design the approach, write the code, and own it in production. This is a hands-on engineering role for someone who is as comfortable in a working session with a business team as they are building a retrieval pipeline or hardening an agent. You will help set the standard for how generative AI gets built and operated responsibly at scale across the firm.

You will:

• Partner directly with business partners to understand their workflows, scope the highest-value opportunities, and translate ambiguous needs into clear technical specifications.

• Design, build, and operate production generative AI applications such as copilots, assistants, knowledge-search experiences, and agentic workflows, as reliable, production-grade systems rather than demos.

• Architect and implement end-to-end retrieval-augmented generation pipelines, including parsing, ingestion, chunking strategy, embeddings, vector storage, retrieval, and prompt management.

• Build agents and agentic workflows that plan and execute multi-step tasks within explicit, auditable boundaries, with guardrails that keep behavior safe and predictable.

• Practice eval-driven development: define acceptance criteria up front, build evaluation harnesses, and measure correctness, latency, and hallucination so quality is verifiable and regressions are caught before production.

• Take end-to-end ownership from discovery and design through build, rollout, and operational excellence. Instrument systems with the observability, cost tracking, and audit trails needed to know when they degrade.

• Apply FinOps and cost-optimization practices to AI workloads, tracking and managing token, inference, and infrastructure spend so solutions stay cost effective as they scale.

• Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment.

• Apply responsible-AI judgment proportionate to the risk of each use case, working with risk and compliance partners to build the controls, human-oversight patterns, and audit trails that let the firm move quickly and safely.

• Embed security, privacy, and compliance controls into the systems you build, including identity and access management (IAM), encryption, and audit logging. Partner with InfoSec and data-governance teams to meet regulatory and internal-policy requirements such as SOC 2 and applicable data-privacy regulations.

• Codify what works into reusable tools, patterns, and playbooks, and feed insights back to platform, product, and engineering partners so the whole organization gets faster.

• Produce clear documentation, runbooks, and architectural diagrams so others can understand, operate, and extend the systems you build.

• Demonstrate the ability to be a full stack engineer across development environments.

Required qualifications:

• 10+ years of professional software engineering experience, with strong proficiency in Python (or a comparable modern language).

• Hands-on production experience building and shipping LLM-powered applications, including advanced prompt engineering, retrieval, agent development, and evaluation.

• Demonstrated experience designing and building end-to-end RAG pipelines and integrating LLM solutions with real systems.

• Strong understanding of system design, APIs, distributed-systems concepts, and cloud-native development, with a track record of owning production systems on solid architectural foundations.

• A disciplined approach to evaluation and testing for non-deterministic systems: you build evals and guardrails as a first-class part of the work, not an afterthought.

• Strong communication skills: you can lead technical discovery, write clearly, and convey technical concepts to mixed audiences while keeping a low ego and a collaborative approach.

• High agency and comfort navigating the ambiguity of a large, regulated organization, with the judgment to make trade-offs between scope, speed, and quality.

• Bachelor’s degree in computer science, Engineering, or a related technical field, or equivalent practical experience.

• Experience implementing security, privacy, and compliance controls in production systems, for example IAM, encryption, audit logging, and data-governance practices, ideally in a regulated environment.

Preferred qualifications:

• Background in financial services or another regulated enterprise environment.

• Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol).

• Experience with MLOps/LLMOps tooling such as experiment tracking, model versioning, monitoring, evaluation/observability platforms, or CI/CD for ML and LLM systems.

• Experience implementing responsible-AI or AI-governance controls such as guardrails, human-in-the-loop oversight, and audit trails in production.

• Experience mentoring engineers or setting technical standards as a senior individual contributor.

What you bring:

• You operate with urgency, ownership, humility, and strong collaboration.

• You hold high standards for code quality, testing, clarity, and reliability, and you apply engineering judgment to know which standard matters where.

• You treat intent and verification as the hard part: you care less about how much code gets written and more about whether it solves the problem, holds up under evaluation, and can be owned safely in production.

• You can say no constructively by pushing back on scope, proposing better alternatives, and protecting quality and team capacity when it counts.

• You stay current as the tooling and model landscape shifts, and you help the people around you do the same.