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Senior AI Engineer
Group 107
40 minutes ago
Full-time
On-site
New York City, New York, United States
IMTC is a NYC-based, venture-backed FinTech company creating innovative technology
solutions for fixed income professionals. IMTC's first-of-its-kind, cloud-based platform enables users to leverage a highly dynamic optimization engine along with a suite of automation tools to manage bond portfolios for retail and institutional clients. The combination of intuitive interfaces and purpose-built tools helps streamline time-intensive workflows so our clients can focus on high-value work. As IMTC continues to grow and serve an expanding roster of top-tier asset managers, we are embedding AI directly into the platform our clients use every day.
We are looking for a Senior AI Engineer to design, build, and operate AI capabilities in our fixed income portfolio management platform. This is a hands-on, product-focused builder role: you will own client-facing AI features end to end, from discovery with product and clients through production rollout, monitoring, and iteration. You will work directly with the CTO, product management, and our principal and financial engineers to make sure AI outputs are accurate, explainable, and fit for a regulated environment serving some of the country's most sophisticated financial institutions.
Responsibilities: Design, build, and ship production LLM-powered features in the IMTC platform, such as natural-language portfolio and holdings queries, plain-language explanations of optimization and rebalancing outcomes, and extraction of client constraints and investment policy documents into structured portfolio rules Partner with product management and clients to identify where AI meaningfully improves portfolio management workflows, and where it does not; turn those insights into a prioritized AI feature roadmap Design AI user experiences with product and front-end engineers: streaming responses, citations back to source data, and human-in-the-loop review before any AI-suggested change reaches a portfolio Build agentic workflows and tool-calling integrations against IMTC's .NET microservices and data stores (Cosmos DB, PostgreSQL, Elasticsearch), including retrieval-augmented generation (RAG) and MCP-based tool exposure Instrument AI features to measure client adoption, accuracy, and business value, and iterate based on usage data and client feedback Own the AI service layer on Azure (Azure OpenAI / AI Foundry, AKS): model selection, prompt and context management, and cost and latency budgets; Python for AI services, C#/.NET (Microsoft Agent Framework, Microsoft.Extensions.AI) for platform integration Build evaluation harnesses and regression test sets so AI quality is measured, not assumed; track accuracy, hallucination rates, latency, and cost in production Implement guardrails for financial services with our financial engineers: grounding in source data, deterministic calculations kept out of the model, consistency with the optimization engine and compliance rules, PII handling, tenant isolation, and audit logging Operate within IMTC's SOC 2 Type II controls (change management, access control, data protection) and provide technical input to client due diligence questionnaires (DDQs) on AI usage
Qualifications: 6+ years of software engineering experience, including 2+ years building and operating LLM- or ML-powered features in production (beyond prototypes) Track record of shipping customer-facing AI features in a B2B SaaS product, working closely with product management and end users Strong Python engineering skills, plus working proficiency in C#/.NET or the ability to ramp quickly in a .NET microservices codebase Hands-on experience with LLM APIs (Azure OpenAI, OpenAI, Anthropic), RAG design (chunking, embeddings, vector and hybrid search), tool/function calling, and agent orchestration Proven practice building evaluations for LLM systems: curated test sets, automated graders, and regression tracking across model and prompt changes Experience deploying and running services on Azure (or another major cloud) using containers and Kubernetes Day-to-day Git proficiency (branching, merging, conflict resolution, PR review) and hands-on authoring of CI/CD pipelines (GitHub Actions or Azure DevOps YAML) Solid understanding of AI security and data protection risks, including prompt injection, data leakage, and access control on retrieved data Ability to explain AI behavior, limitations, and risks clearly to non-technical stakeholders and clients Background in financial services, asset management, or fixed income is a strong advantage Experience with agent frameworks (e.g. Microsoft Agent Framework / Semantic Kernel, LangGraph), MCP, Kafka, Elasticsearch, or Angular is a plus
solutions for fixed income professionals. IMTC's first-of-its-kind, cloud-based platform enables users to leverage a highly dynamic optimization engine along with a suite of automation tools to manage bond portfolios for retail and institutional clients. The combination of intuitive interfaces and purpose-built tools helps streamline time-intensive workflows so our clients can focus on high-value work. As IMTC continues to grow and serve an expanding roster of top-tier asset managers, we are embedding AI directly into the platform our clients use every day.
We are looking for a Senior AI Engineer to design, build, and operate AI capabilities in our fixed income portfolio management platform. This is a hands-on, product-focused builder role: you will own client-facing AI features end to end, from discovery with product and clients through production rollout, monitoring, and iteration. You will work directly with the CTO, product management, and our principal and financial engineers to make sure AI outputs are accurate, explainable, and fit for a regulated environment serving some of the country's most sophisticated financial institutions.
Responsibilities: Design, build, and ship production LLM-powered features in the IMTC platform, such as natural-language portfolio and holdings queries, plain-language explanations of optimization and rebalancing outcomes, and extraction of client constraints and investment policy documents into structured portfolio rules Partner with product management and clients to identify where AI meaningfully improves portfolio management workflows, and where it does not; turn those insights into a prioritized AI feature roadmap Design AI user experiences with product and front-end engineers: streaming responses, citations back to source data, and human-in-the-loop review before any AI-suggested change reaches a portfolio Build agentic workflows and tool-calling integrations against IMTC's .NET microservices and data stores (Cosmos DB, PostgreSQL, Elasticsearch), including retrieval-augmented generation (RAG) and MCP-based tool exposure Instrument AI features to measure client adoption, accuracy, and business value, and iterate based on usage data and client feedback Own the AI service layer on Azure (Azure OpenAI / AI Foundry, AKS): model selection, prompt and context management, and cost and latency budgets; Python for AI services, C#/.NET (Microsoft Agent Framework, Microsoft.Extensions.AI) for platform integration Build evaluation harnesses and regression test sets so AI quality is measured, not assumed; track accuracy, hallucination rates, latency, and cost in production Implement guardrails for financial services with our financial engineers: grounding in source data, deterministic calculations kept out of the model, consistency with the optimization engine and compliance rules, PII handling, tenant isolation, and audit logging Operate within IMTC's SOC 2 Type II controls (change management, access control, data protection) and provide technical input to client due diligence questionnaires (DDQs) on AI usage
Qualifications: 6+ years of software engineering experience, including 2+ years building and operating LLM- or ML-powered features in production (beyond prototypes) Track record of shipping customer-facing AI features in a B2B SaaS product, working closely with product management and end users Strong Python engineering skills, plus working proficiency in C#/.NET or the ability to ramp quickly in a .NET microservices codebase Hands-on experience with LLM APIs (Azure OpenAI, OpenAI, Anthropic), RAG design (chunking, embeddings, vector and hybrid search), tool/function calling, and agent orchestration Proven practice building evaluations for LLM systems: curated test sets, automated graders, and regression tracking across model and prompt changes Experience deploying and running services on Azure (or another major cloud) using containers and Kubernetes Day-to-day Git proficiency (branching, merging, conflict resolution, PR review) and hands-on authoring of CI/CD pipelines (GitHub Actions or Azure DevOps YAML) Solid understanding of AI security and data protection risks, including prompt injection, data leakage, and access control on retrieved data Ability to explain AI behavior, limitations, and risks clearly to non-technical stakeholders and clients Background in financial services, asset management, or fixed income is a strong advantage Experience with agent frameworks (e.g. Microsoft Agent Framework / Semantic Kernel, LangGraph), MCP, Kafka, Elasticsearch, or Angular is a plus