Job VC
Middle Software Engineer – AI Voice Systems (Python/TypeScript)
Technologies
Description
We are looking for a Middle Software Developer with a strong Python background, solid experience with production services, and willingness to work with or gradually move toward TypeScript.
The ideal candidate is not just a code executor, but an engineer interested in complex integrations, service reliability, real-time workflows, AI/LLM integrations, and infrastructure transformation.
This role is a good fit for someone who wants to work in a small fast-scaling team, take ownership, and contribute to the core platform rather than just complete tickets.
Customer
The customer is a product company developing a platform for automating customer communications using AI-based solutions.
The team is small, fast-scaling, and operates with a low level of bureaucracy. The role involves direct communication with the customer team and a strong ownership-oriented engineering culture.
The project is in an active development phase. The candidate will work on backend services, external provider integrations, and contribute to the ongoing development of the existing platform.
Project
The project is an agentic call center platform that uses Voice and Text AI agents to conduct real customer conversations, automate workflows, and integrate with CRMs and telecom providers.
The platform helps businesses automate customer communication at scale, including bookings, call transfers, CRM workflows, messaging, and routing.
From a technical perspective, the project is interesting because it includes Python to TypeScript migration, multi-LLM orchestration, real-time voice AI, low-latency workflows, MongoDB, Aurora, RabbitMQ, Redis, Docker/ECS, and a future migration to Kubernetes.
Key Technologies:
Python, TypeScript (Node.js), MongoDB, Aurora (RDS), RabbitMQ, Redis, Docker, ECS, Kubernetes, AWS, multi-LLM orchestration, real-time voice AI
Responsibilities
Develop and maintain services ensuring clean, testable, well-documented code
Build and improve APIs and services orchestrating agent workflows across LLM/TTS/STT vendors
Implement resilient patterns for external provider calls (retries, timeouts, fallbacks, rate limits, circuit breaking)
Integrate with CRMs and telecom providers, enabling bi-directional sync and workflow triggers
Maintain internal abstractions/adapters for consistent and extensible integrations
Work with MongoDB and Aurora for data storage and relational guarantees
Use RabbitMQ for asynchronous workflows and Redis for caching and ephemeral state
Participate in migration planning from Docker + ECS to Kubernetes
Contribute to CI/CD improvements, deployment safety, and observability
Engage in incident response and operational reliability improvements
Requirements
3+ years of backend engineering experience with hands-on ownership of production services
Strong proficiency in Python
Good understanding of backend engineering fundamentals: APIs, data modeling, and basic distributed systems concepts
Willingness to work with TypeScript or eagerness to learn it
Experience with Docker and containerized services
Practical experience with MongoDB or a relational database such as PostgreSQL / Aurora
Understanding of basic schema design and query optimization
Familiarity with messaging systems such as RabbitMQ or similar tools
Experience with Redis for caching, rate limiting, or ephemeral state
Strong debugging skills
Interest in operational topics: logging, monitoring, service reliability
Proactive communication and ability to take a well-scoped problem and drive it to completion
Ability to work in a small team with direct customer communication
WILL BE A PLUS
Experience with real-time systems: telephony, WebSockets, streaming, low-latency pipelines
Familiarity with vendor orchestration patterns: fallback routing, provider selection
Any Kubernetes experience, even in dev/staging environments
Exposure to security/compliance concepts: SOC 2, secrets management, audit trails
Experience with AI/LLM, TTS/STT, or voice-related systems
Personal Profile
Proactive and ownership-driven mindset
Enjoys solving complex integration challenges
Interested in cutting-edge AI voice systems
Strong problem-solving skills
Effective communicator in distributed teams
The ideal candidate is not just a code executor, but an engineer interested in complex integrations, service reliability, real-time workflows, AI/LLM integrations, and infrastructure transformation.
This role is a good fit for someone who wants to work in a small fast-scaling team, take ownership, and contribute to the core platform rather than just complete tickets.
Customer
The customer is a product company developing a platform for automating customer communications using AI-based solutions.
The team is small, fast-scaling, and operates with a low level of bureaucracy. The role involves direct communication with the customer team and a strong ownership-oriented engineering culture.
The project is in an active development phase. The candidate will work on backend services, external provider integrations, and contribute to the ongoing development of the existing platform.
Project
The project is an agentic call center platform that uses Voice and Text AI agents to conduct real customer conversations, automate workflows, and integrate with CRMs and telecom providers.
The platform helps businesses automate customer communication at scale, including bookings, call transfers, CRM workflows, messaging, and routing.
From a technical perspective, the project is interesting because it includes Python to TypeScript migration, multi-LLM orchestration, real-time voice AI, low-latency workflows, MongoDB, Aurora, RabbitMQ, Redis, Docker/ECS, and a future migration to Kubernetes.
Key Technologies:
Python, TypeScript (Node.js), MongoDB, Aurora (RDS), RabbitMQ, Redis, Docker, ECS, Kubernetes, AWS, multi-LLM orchestration, real-time voice AI
Responsibilities
Develop and maintain services ensuring clean, testable, well-documented code
Build and improve APIs and services orchestrating agent workflows across LLM/TTS/STT vendors
Implement resilient patterns for external provider calls (retries, timeouts, fallbacks, rate limits, circuit breaking)
Integrate with CRMs and telecom providers, enabling bi-directional sync and workflow triggers
Maintain internal abstractions/adapters for consistent and extensible integrations
Work with MongoDB and Aurora for data storage and relational guarantees
Use RabbitMQ for asynchronous workflows and Redis for caching and ephemeral state
Participate in migration planning from Docker + ECS to Kubernetes
Contribute to CI/CD improvements, deployment safety, and observability
Engage in incident response and operational reliability improvements
Requirements
3+ years of backend engineering experience with hands-on ownership of production services
Strong proficiency in Python
Good understanding of backend engineering fundamentals: APIs, data modeling, and basic distributed systems concepts
Willingness to work with TypeScript or eagerness to learn it
Experience with Docker and containerized services
Practical experience with MongoDB or a relational database such as PostgreSQL / Aurora
Understanding of basic schema design and query optimization
Familiarity with messaging systems such as RabbitMQ or similar tools
Experience with Redis for caching, rate limiting, or ephemeral state
Strong debugging skills
Interest in operational topics: logging, monitoring, service reliability
Proactive communication and ability to take a well-scoped problem and drive it to completion
Ability to work in a small team with direct customer communication
WILL BE A PLUS
Experience with real-time systems: telephony, WebSockets, streaming, low-latency pipelines
Familiarity with vendor orchestration patterns: fallback routing, provider selection
Any Kubernetes experience, even in dev/staging environments
Exposure to security/compliance concepts: SOC 2, secrets management, audit trails
Experience with AI/LLM, TTS/STT, or voice-related systems
Personal Profile
Proactive and ownership-driven mindset
Enjoys solving complex integration challenges
Interested in cutting-edge AI voice systems
Strong problem-solving skills
Effective communicator in distributed teams