AI Engineer

Job Description

Job Description

Our client a leading asset management firm, located in New York City is looking to hire an AI Engineer to join their Business Technology and Data Team. This is a Full Time Position. Work will be conducted 3 days in office and 2 day from home. Citizens and those authorized to work in the U.S. are encouraged to apply. Qualified candidates should send their resumes (Word Format) along with salary expectations to View email address on ziprecruiter.com .

SUMMARY
The AI Engineer will work closely with the Investment Research team to design, build, and support AI-enabled tools that improve research productivity, insight generation, knowledge discovery, and investment decision support. This role will partner with research analysts, portfolio teams, business technology, data, compliance, and operations stakeholders to identify high-value AI use cases and deliver practical, secure, and scalable AI products.
The ideal candidate has a strong AI/ML foundation and hands-on experience building AI products, including generative AI applications, retrieval-augmented generation, natural language processing, model evaluation, prompt engineering, data pipelines, and production-ready software engineering practices. The ideal candidate will support AI initiatives within the investment research area while helping establish reusable patterns, standards, and governance for responsible AI adoption.

RESPONSIBILITIES

• Partner with the Investment Research team to identify, prioritize, and deliver AI use cases that support research workflows, knowledge management, document analysis, data analysis, and decision support.
• Design and build AI products and tools, including generative AI assistants, research copilots, summarization tools, semantic search, document intelligence, workflow automation, and analytics-enabled applications.
• Develop retrieval-augmented generation solutions using structured and unstructured data sources, including research notes, filings, earnings transcripts, market data, internal documents, and approved external content.
• Build and maintain data ingestion, preprocessing, embedding, vector search, model orchestration, and evaluation pipelines to support reliable AI applications.
• Apply strong AI/ML, natural language processing, and software engineering practices to design scalable, secure, maintainable, and user-friendly solutions.
• Evaluate models, prompts, data sources, outputs, and user feedback to improve accuracy, relevance, explainability, and adoption of AI tools.
• Collaborate with data architects, data engineers, application developers, infrastructure, cybersecurity, and compliance teams to integrate AI solutions with enterprise platforms and controls.
• Implement responsible AI practices, including access controls, human-in-the-loop review, auditability, data privacy, model risk awareness, content validation, and appropriate usage guidelines.
• Prototype quickly with business users, translate research needs into technical requirements, and move successful prototypes into supported production solutions.
• Create documentation for AI solution architecture, data flows, model behavior, prompt patterns, evaluation results, operating procedures, and support processes.
• Monitor emerging AI technologies, tools, frameworks, and investment research use cases, and recommend practical opportunities for adoption.
• Provide ongoing support, troubleshooting, enhancements, and user training for AI tools deployed within the investment research area.

REQUIREMENTS

• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field; Master's preferred.

• 3 5 years of experience in AI/ML engineering, applied ML, or software engineering, with at least one AI application running in production.
• Retrieval-augmented generation (RAG) end to end: document chunking strategy, embedding models, vector search, hybrid and metadata-filtered retrieval, and measuring retrieval quality (View email address on ziprecruiter.com, relevance evaluation sets).
• Embeddings in practice: choosing and migrating between embedding models (e.g. OpenAI, Gemini, open-source), handling token and context limits, caching, and re-indexing large corpora without downtime.
• Vector and relational databases: strong SQL and PostgreSQL, including pgvector or a comparable vector store (Qdrant, Weaviate, Milvus). Schema design for chunks, metadata, and multiple embedding versions.
• LLM application engineering: prompt engineering, structured outputs, tool and function calling, agent workflows (including MCP or similar tool protocols), evaluation, and guardrails. Experience with Anthropic Claude and/or OpenAI APIs.
• Production Python: batch pipelines that hold up at scale (memory-bounded processing, retries, API rate and token limits, idempotent re-runs), plus testing, version control (Git), logging, and clear documentation.
• Unstructured financial documents: extracting and structuring SEC filings (10-K, 10-Q, 8-K, 20-F, 6-K), earnings-call transcripts, PDFs, and HTML, and cleaning messy enterprise data.
• Data analysis: profiling, validating, and reconciling data from market-data providers (e.g. FactSet) and internal sources to support AI outputs.
• Working with stakeholders works directly with portfolio managers and analysts to gather requirements, ships fast iterations, and explains AI trade-offs (accuracy, cost, latency) in plain language.
• Governance: understands data privacy, security, and responsible AI in a regulated investment firm, including handling material non-public information (MNPI), access controls, auditability, and AI usage policy.

PREFERRED QUALIFICATIONS

• Experience in investment management, equity research, or financial services.
• Scheduling and operations for data pipelines (Windows Task Scheduler, cron, Airflow or similar), and deploying to Azure and/or IIS.
• Building lightweight internal tools and dashboards (HTML/JavaScript, charting libraries) on top of AI pipelines.
• Experience setting up governed self-service AI for business users, such as shared prompt projects and review-and-deploy workflows.
• Familiarity with LLM cost monitoring and model-selection trade-offs.

KNOWLEDGE, SKILLS, AND ABILITIES

• Product-oriented mindset with the ability to move from prototype to reliable enterprise solution.
• Strong analytical, problem-solving, and critical-thinking skills with attention to accuracy, usability, and business value.
• Ability to collaborate effectively with investment professionals, technology teams, data teams, and control functions.
• Strong communication skills and the ability to explain AI capabilities, limitations, risks, and trade-offs to technical and non-technical audiences.
• Curiosity and continuous learning orientation in a rapidly evolving AI technology landscape.
• Strong documentation discipline, including solution design, testing results, support procedures, and user guidance.

Salary 160k