Agentic Software Engineer, Data Platform

Agentic Software Engineer, Data Platform

Posted 75 Days Ago

With respect to NY, CA, and IL based applicants, the starting base pay range for this role is between USD 175000 and USD 250000 annually. The actual base pay is dependent upon several factors, including, but not limited to, relevant experience, business needs and market demands. This role may also be eligible for bonus compensation and employee benefits.

About Longaeva Partners

Longaeva: Pronounced “long-AY-vuh”, our name is rooted in meaning! We are named after one of the oldest and most resilient living species, the longaeva pine. It represents longevity, adaptability, and persistence. It thrives in extreme conditions and has adapted for thousands of years in challenging environments. We hope to do the same, delivering positive, risk-adjusted returns to our investors, even in dynamic and demanding market conditions.

We are a New York based hedge fund with a global investment mandate focusing on long duration investing across long/short public equities and late-stage private deals. Our founder and CIO, Peter Goodwin, bringing nearly 20 years of investing experience across the healthcare, consumer and TMT sectors.

We are a high-conviction, ideas-driven firm built on collaboration, rigorous primary research, and data-driven decision making. A core part of our approach is leveraging advanced artificial intelligence and technology to enhance our research, generate insights, and drive better investment outcomes. At Longaeva, you’ll join a team that values intellectual curiosity, diverse viewpoints, and a shared commitment to uncovering differentiated investment opportunities.

Role Overview

We are looking for an Agentic Software Engineer to build the data platforms, infrastructure, and data products that enable investment professionals and business users to safely create AI-powered workflows and applications.

The role’s core mission is to build the data platform, APIs, workflow infrastructure, and self-serve tooling that make AI useful across the organization. You will create the foundations that let non-developers work with AI in a fast, secure, reliable, and scalable way - including curated data products and API, workflow frameworks, application scaffolding, and controlled interfaces to internal data and services.

This role also requires a modern engineering mindset. The ideal candidate is excited by the emerging world of agentic engineering and harness engineering, where engineers direct multiple AI agents to produce high-quality production code at high velocity.

The ideal candidate combines strong technical execution, solid software design and coding principles, data pipeline construction, and sharp product intuition, with a proven ability to ship independently and at high velocity.

Key Responsibilities

Build the AI-Native Data Platform:

·      Design and build the core data platform that powers AI applications, analytics, and operational workflows.

·      Develop secure, scalable, reliable, and discoverable data products for both humans and AI systems.

·      Create data access patterns that are easy to use, safe by default, and robust at scale.

·      Build and maintain APIs and service layers on top of internal data to support downstream applications, automation, and AI workflows.

·      Contribute to semantic and application-facing data layers so users can interact with clean, business-meaningful abstractions rather than raw source systems.

Enable Non-Developers to Build with AI

·      Build platform capabilities that allow investment staff and business users to create AI-powered applications and workflows without requiring deep engineering support.

·      Design safe self-serve patterns for prompt-backed workflows, retrieval-based apps, internal copilots, structured automation, and task-specific AI tools.

·      Create reusable templates, connectors, workflow abstractions, approval steps, permissions models, and operational guardrails.

·      Ensure these capabilities are secure, observable, auditable, and easy to adopt.

Build APIs, Workflows, and Application Infrastructure

·      Develop APIs and backend services that expose internal data and business logic for use by internal apps, agents, and workflows.

·      Build workflow engines and orchestration layers that combine data retrieval, business rules, model calls, and human review where needed.

·      Design systems that support asynchronous, long-running, and stateful business processes.

·      Create reusable platform primitives so teams can assemble AI-enabled use cases quickly without rebuilding infrastructure from scratch.

Practice High-Leverage Agentic Engineering

·      Use modern AI-native development workflows to accelerate engineering output dramatically from 10x to 100x

·      Operate effectively in an environment where engineers direct multiple agents in parallel for coding, testing, refactoring, documentation, and debugging.

·      Use these methods to ship production code quickly and continuously while maintaining quality.

Qualifications

Education

• Degree in Computer Science, Data Science, Statistics, Mathematics, Physics, or equivalent work experience.
• Academic training in computer science, statistics, and data science techniques, including machine learning methods, and generative AI techniques.

Experience

·      Strong software engineering fundamentals with experience building production backend systems, platforms, or data infrastructure.

·      Proven experience in system design and architecture for large-scale or business-critical platforms.

·      Experience building APIs, services, data pipelines, or workflow infrastructure.

·      Experience with modern cloud architecture (AWS or other clouds), distributed systems, and platform operations.

·      Strong proficiency in Python and SQL.

·      Experience building data products, internal platforms, or self-serve infrastructure for technical or non-technical users.

·      Deep curiosity about AI-native development and enthusiasm for working in a world where multiple agents are part of the engineering loop.

·      Hands-on experience building pipelines that integrate real-time and batch data sources across structured and unstructured formats.

·      Experience with LLM applications, vector database, graph databases, retrieval systems, semantic search, AI orchestration, multi-agent frameworks.

Soft Skills

• The ideal candidate is entrepreneurial, high energy, and eager to operate with high urgency, ownership, and creativity in a fast-moving environment.
• Strong problem-solving and analytical skills, with the ability to synthesize complex, multi-source data into clear, actionable outputs.
• Excellent communication and interpersonal skills; able to translate technical findings into formats appropriate for PMs, risk officers, and traders.
• Demonstrated ability to manage multiple workstreams, prioritize under pressure, and meet deadlines in a fast-moving investment environment.
• Ability to work independently and as part of a cross-functional team spanning many business teams, including Investment, Data Science, Risk, and Trading.