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AI Engineer

Velocity Search Group Inc.

New York, New York • Today

Easy Apply

Full-time

$150000 - $170000

Founding AI Engineer Agentic AI, Shipping, Production

Avacend, Inc.

Hybrid in New York, New York • Today

Easy Apply

Full-time

Depends on Experience

Lead Applied AI Engineer

Innova Solutions, Inc

New York, New York • 8d ago

Easy Apply

Contract

Depends on Experience

AI Engineer

Synechron

Hybrid in New York, New York • 28d ago

Easy Apply

Full-time

115000 - 130000

AI Engineer

NxT Level

New York, New York • Today

Full-time

AI Engineer

Judge Group, Inc.

Jersey City, New Jersey • Today

Contract

Compensation information provided in the description

Palantir AI Engineer

Marici Solutions

Jersey City, New Jersey • 27d ago

Easy Apply

Contract, Third Party

AI Engineer

Compunnel Inc.

Newark, New Jersey • Today

Easy Apply

Contract

DOE

Agentic AI Engineer

Yotta Systems INC

New York, New York • 6d ago

Easy Apply

Full-time

Depends on Experience

AI Engineer - Vice President

iCapital

New York, New York • Today

Full-time

USD 170,000.00 - 210,000.00 per year

Applied AI Engineer

Software Guidance & Assistance

New York, New York • Today

Contract

Staff AI Engineer

Capital One

New York, New York • Today

Full-time

AI Engineer 4

Capital One

New York, New York • Today

Full-time

Staff AI Engineer

Via

New York, New York • Today

Full-time

USD 250,000.00 - 300,000.00 per year

Principal AI Engineer

Vertex

New York, New York • 13d ago

Full-time

USD 159,600.00 - 207,500.00 per year

AI Engineer, Product

Welltower

New York, New York • Today

Full-time

USD 160,000.00 - 220,000.00 per year

Akamai with AI Engineer

IMR Soft LLC

Hybrid in New York, New York • 8d ago

Easy Apply

Third Party, Contract

$60 - $65

Akamai with AI Engineer

Noblesoft Technologies Inc.

Hybrid in New York, New York • 8d ago

Easy Apply

Third Party, Contract

Depends on Experience

AI Engineer 5 (MLXT)

Capital One

New York, New York • Today

Full-time

AI Engineer 4 (MLX)

Capital One

New York, New York • Today

Full-time

AI Engineer - Assistant Vice President

iCapital

New York, New York • Today

Full-time

USD 120,000.00 - 170,000.00 per year

Forward Deployed AI Engineer

Select Solutions Group, LLC

Hybrid in New York, New York • Yesterday

Easy Apply

Contract

100 - 120

Applied AI Engineer

MaintainX

New York, New York • Today

Full-time

USD 152,000.00 - 271,000.00 per year

AI Engineer

Brilliant Infotech Inc.

Edison, New Jersey • Today

Easy Apply

Contract, Third Party

Depends on Experience

Lead Python AI Engineer

EPAM Systems

Remote or New York, New York • Today

Full-time

Principal AI Engineer

CogniSoft Technologies

Hybrid in New York, New York • 6d ago

Easy Apply

Full-time

Depends on Experience

Agentic AI Engineer

Reveille Technologies

New York, New York • 6d ago

Easy Apply

Full-time

Depends on Experience

AI Engineer

4i Americas

Jersey City, New Jersey • Today

Easy Apply

Contract

Depends on Experience

AI Engineer- Fulltime, NO C2C, onsite in Englewood Cliffs, NJ

Marlabs LLC

Englewood Cliffs, New Jersey • Yesterday

Easy Apply

Full-time

Depends on Experience

AI Engineer 5 (AI Foundations)

Capital One

New York, New York • Today

Full-time

Job Details

Skills

Summary

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 .

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 (recall@k, 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

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 80108439
Position Id: 26-00024
Posted 3 hours ago

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Skills

Summary

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 (recall@k, 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

Dice Id: 80108439
Position Id: 26-00024
Posted 3 hours ago

Fitment

Dice Job Match Score™

To see how well you match this job, please log in or create an account.

Once logged in, be sure to complete your profile to get the most accurate match score.

Create job alert

Never miss an opportunity! Create an alert based on the job you applied for.

Similar Jobs

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