Intellectsoft is a software development company delivering innovative solutions since 2007. We operate across North America, Latin America, the Nordic region, the UK, and Europe.We specialize in industries like Fintech, Healthcare, EdTech, Construction, Hospitality, and more, partnering with startups, mid-sized businesses, and Fortune 500 companies to drive innovation and scalability. Our clients include Jaguar Motors, Universal Pictures, Harley-Davidson, and many more where our teams are making daily impact. Together, our team delivers solutions that make a difference. Learn more at 're building a declarative layer for financial research. Think of it like Vercel for financial research - just as Vercel lets you spin up a new project with vercel init and handles all the infrastructure behind the scenes, we want to be able to declare an array of financial research processes and have the system handle orchestration, execution, and delivery. The goal is to make spawning new research representations as frictionless as starting a new Vercel project - define what you want, and the platform takes care of the rest.
La experiencia que se espera de los solicitantes, así como las habilidades y cualificaciones adicionales necesarias para este trabajo, se enumeran a continuación.
Requirements
Expert-level Python proficiency, as it is a primary language for all development.
Deep technical knowledge of relational databases (PostgreSQL), including schema normalization, writing complex joins, query optimization, and managing indexing for high-concurrency environments.
Proficiency with NoSQL Databases, with a deep understanding of when to use document, key-value, or vector stores to handle unstructured LLM outputs and high-velocity research data.
Comprehensive knowledge of deployment approaches, including experience with CI/CD pipelines, containerization (Docker/Kubernetes), and different deployment strategies.
Multi-platform deployment experience, with the ability to navigate and deploy across various environments ranging from serverless to traditional cloud providers (AWS, GCP, or Azure).
Proven experience with serverless compute (e.G., Modal) and cloud-native architectures.
Hands-on experience with LLM APIs including direct integration with OpenAI, Anthropic, xAI, or Google. xhfqzwm
Familiarity with AI orchestration frameworks such as PydanticAI.
High level of resourcefulness and the ability to quickly master new concepts as the AI landscape evolves
Nice to have skills:
Experience working with financial data providers like S&P Global, FactSet, or SEC Edgar
Familiarity with modern AI tooling APIs such as Exa or Parallel
Responsibilities:
Write clean, maintainable Python code to build out the core orchestration engine and API layers
Develop and debug durable execution logic, implementing state management, automated retries, and cost-control triggers for long-running tasks
Build and maintain integrations for LLM providers and financial data APIs, ensuring high availability and low latency
Design and implement database schemas in PostgreSQL and NoSQL stores, writing optimized queries and managing migrations
Create unified API abstractions that allow the system to switch between different LLM models and storage backends via configuration
Build automated test suites and evaluation pipelines to validate the accuracy of LLM outputs and system performance
Configure and automate deployment pipelines across multiple platforms, including CI/CD orchestration and container management
Build and manage scalable infrastructure using serverless compute (Modal) and cloud-native services to ensure high availability
Benefits
Awesome projects with an impact
Udemy courses of your choice
Team-buildings, events, marathons & charity activities to connect and recharge
Workshops, trainings, expert knowledge-sharing that keep you growing
Clear career path
Absence days for work-life balance
Flexible hours & work setup - work from anywhere and organize your day your way
#J-18808-Ljbffr
or continue with email
La experiencia que se espera de los solicitantes, así como las habilidades y cualificaciones adicionales necesarias para este trabajo, se enumeran a continuación.
Requirements
Expert-level Python proficiency, as it is a primary language for all development.
Deep technical knowledge of relational databases (PostgreSQL), including schema normalization, writing complex joins, query optimization, and managing indexing for high-concurrency environments.
Proficiency with NoSQL Databases, with a deep understanding of when to use document, key-value, or vector stores to handle unstructured LLM outputs and high-velocity research data.
Comprehensive knowledge of deployment approaches, including experience with CI/CD pipelines, containerization (Docker/Kubernetes), and different deployment strategies.
Multi-platform deployment experience, with the ability to navigate and deploy across various environments ranging from serverless to traditional cloud providers (AWS, GCP, or Azure).
Proven experience with serverless compute (e.G., Modal) and cloud-native architectures.
Hands-on experience with LLM APIs including direct integration with OpenAI, Anthropic, xAI, or Google. xhfqzwm
Familiarity with AI orchestration frameworks such as PydanticAI.
High level of resourcefulness and the ability to quickly master new concepts as the AI landscape evolves
Nice to have skills:
Experience working with financial data providers like S&P Global, FactSet, or SEC Edgar
Familiarity with modern AI tooling APIs such as Exa or Parallel
Responsibilities:
Write clean, maintainable Python code to build out the core orchestration engine and API layers
Develop and debug durable execution logic, implementing state management, automated retries, and cost-control triggers for long-running tasks
Build and maintain integrations for LLM providers and financial data APIs, ensuring high availability and low latency
Design and implement database schemas in PostgreSQL and NoSQL stores, writing optimized queries and managing migrations
Create unified API abstractions that allow the system to switch between different LLM models and storage backends via configuration
Build automated test suites and evaluation pipelines to validate the accuracy of LLM outputs and system performance
Configure and automate deployment pipelines across multiple platforms, including CI/CD orchestration and container management
Build and manage scalable infrastructure using serverless compute (Modal) and cloud-native services to ensure high availability
Benefits
Awesome projects with an impact
Udemy courses of your choice
Team-buildings, events, marathons & charity activities to connect and recharge
Workshops, trainings, expert knowledge-sharing that keep you growing
Clear career path
Absence days for work-life balance
Flexible hours & work setup - work from anywhere and organize your day your way
#J-18808-Ljbffr
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