Senior AI Engineer

Location: New York

Senior AI Engineer page is loaded## Senior AI Engineer locations:
New York, New York, United States of America time type:
Full time posted on:
Posted 15 Days Agojob requisition :
866002##
** Senior AI Engineer, Business Solutions**### Clear Bridge Investments is a leading global equity manager with over $180 billion in assets under management.  The firm is committed to delivering long-term results through active management. We have followed this approach for more than 60 years and continue to offer investment solutions that emphasize differentiated stock selection to move our clients forward. Our investment decisions are supported by a robust research platform that conducts in-depth fundamental analysis of individual securities.

We integrate environmental, social and governance (ESG) considerations into our fundamental research process in all our investment strategies.### Owned by Franklin Templeton, Clear Bridge operates with investment independence from headquarters in New York and offices in Baltimore, Calgary, Fort Lauderdale, London, San Mateo and Sydney.
Clear Bridge Investments has been named by Pensions & Investments Magazine (P&I) as one of the Best Places to Work in Money Management for the thirteenth year in a row. These awards reflect the Firm's diverse and inclusive culture, spirit of collaboration, work/life balance and our ongoing investment in our employees.###
** Job Summary
** Clear Bridge Investments seeks a Senior AI Engineer to join our New York City–based Technology team to design, build, and product ionize AI systems that materially improve investment and enterprise workflows. This role is hands-on. You will prototype quickly, harden what works, and decide when to build with APIs, extend our enterprise AI platforms, or buy from vendors. You will integrate ChatGPT Enterprise, Perplexity Enterprise, Microsoft Copilot, and Wand.ai

with our Microsoft-centric stack, enforce governance, and deliver measurable outcomes for research, portfolio operations, client reporting, distribution, and support teams. Location is New York City hybrid.###
** Key Senior Business Engineer Responsibilities
*** Ship AI features and internal applications from discovery to POC to production with clear success criteria and telemetry.
* Architect LLM solutions including retrieval augmented generation, tool use, agentic flows, and structured output pipelines.
* Integrate and govern ChatGPT Enterprise, Perplexity Enterprise, and Wand.ai. Implement SSO and SCIM, usage analytics, cost controls, and data scoping.
* Own build versus buy. Run vendor pilots, compare to internal builds, quantify TCO, lock in risk, compliance posture, and speed to value. Present recommendations and rollout plans.
* Implement LLMOps. Version prompts and configs, stand up evaluation datasets and harnesses, run offline and online tests, monitor latency, quality, safety, and cost, and maintain rollback strategies.
* Engineer reliable retrieval over access-controlled content. Design chunking, embeddings, reranking, and output validation aligned to entitlements, PII, retention, and audit needs.
* Convert low code prototypes into maintainable services where appropriate. Keep core logic in tested code with APIs, workers, and queues.
* Partner with stakeholders to frame problems, map processes, and quantify ROI. Produce concise design docs, run demos, and publish runbooks.
* Collaborate with security, risk, and compliance to define guardrails, incident playbooks, and monitoring for prompt injection and data leakage.
* Coordinate with developers, QA, and UAT to move solutions through environments with CI and automated checks.##
** Qualifications**### Required
* 7 to 10 years in software or ML engineering shipping production systems, including 2 or more years building with LLMs in production.
* Strong coding in Python or Type Script.

Experience with APIs, workers, queues, and event driven services.
* Proven integrations with enterprise AI platforms. Expertise in SSO, SCIM, tenant isolation, data scoping, usage analytics, and cost management.
* LLM solution patterns. Retrieval design, chunking, embeddings, reranking, tool calling, structured output…