AI Engineer - L1

Overview

Role Summary

We're looking for an early-career AI Engineer who loves to build and is hungry to grow fast in a real-world, high-stakes environment.

This is a role for someone at the start of their journey who learns by doing - shipping real features, reading other people's code to understand how production systems fit together, and steadily taking on more ownership. You'll work across backend services, web applications, dashboards, and the data layer, contributing to the platforms that power voice AI agents deployed for enterprise clients.

The Engineering team ships production-grade systems end-to-end - from APIs, data pipelines, and campaign orchestration through UI, evaluation tooling, deployment, and post-launch iteration. As an L1, you'll start by owning well-scoped features and gradually grow into larger areas of the platform. You'll build pieces of the dashboards that let teams monitor live calls, the tools that let conversational AI engineers configure and test agents, and the client-facing surfaces that bring it all together. You'll work closely with senior engineers who will mentor you, review your code, and help you level up quickly.

Responsibilities

• Build and ship well-scoped features across the stack - backend services, APIs, frontend interfaces, and data layers - taking each from implementation through testing and deployment, with guidance from senior engineers.
• Contribute to the platform services that power voice AI at scale: campaign orchestration, dialer logic, call routing, scheduling, and the integrations that tie telephony to the application layer.
• Learn to write code that holds up in production - handling errors gracefully, respecting performance constraints, and improving reliability as you go.
• Work with data models, queries, and pipelines (PostgreSQL, time-series and CDR data) to support call processing, reporting, and analytics.
• Debug issues across the stack with growing independence - reading logs, reproducing problems, and tracing whether a failure comes from application code, the database, an integration, or infrastructure.
• Integrate third-party services and provider APIs (telephony, LLM/ASR/TTS, messaging, payments) into features, learning to handle errors, rate limits, and retries robustly.
• Build features with scale and resilience in mind, learning patterns for concurrency, retries, and graceful degradation under production load.
• Follow and absorb the team's engineering best practices around code quality, testing, version control, and CI/CD - and steadily raise your own bar.
• Communicate clearly in code reviews, design discussions, and documentation, asking good questions and surfacing blockers early.

Desired Profile

• 1+ years of experience building software (strong internships, substantial personal/academic projects, or open-source contributions count toward this).
• Hands-on experience writing backend services or APIs, with some exposure to frontend work.
• Working knowledge of Python; familiarity with Django (or a similar web framework) is a plus.
• Comfortable using AI coding and productivity tools (e.g. Claude Code, Cursor, Copilot) and eager to make them a core part of how you build and ship.
• Familiarity with relational databases (PostgreSQL preferred) - writing queries, understanding basic schema design, and working with real data.
• Some exposure to or genuine curiosity about cloud infrastructure (AWS preferred — EC2, RDS/Aurora, S3) and concepts like deployment, containerization, and CI/CD.
• A developing debugging mindset - willing to dig into logs, trace a request, and figure out where something broke instead of guessing.
• Clear written communication for code reviews, design notes, and documentation.
• Curious, coachable, and a fast learner, motivated by shipping real products used by real customers and by feedback that helps you grow.

Bonus if you have experience in:

• DevOps practices and tooling, CI/CD pipelines, containerization (Docker), or managing deployments across environments.
• FreeSWITCH or similar telephony platforms, or even a working understanding of how an application layer integrates with a voice stack.
• AWS data services such as Glue and Athena, or any experience building ETL pipelines and querying large-scale data.
• Building dashboards and reporting layers that turn operational data into useful insight.
• Security and compliance basics in production systems, especially for regulated (BFSI) clients.
• Telephony/SIP, real-time systems, or other latency-sensitive production infrastructure.