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Who We Are: Incredibuild is the leading platform empowering developers and enterprises to radically accelerate their development cycles. We help world-leading brands like Microsoft, Citi Bank, GM, Amazon, and Adobe shorten build times, allowing for more iterations, faster product releases, and significant resource savings. Our technology streamlines and accelerates everything from compilation to release automation, dramatically expediting time to market for our clients. You aren’t just "fixing CI/CD pipelines" - you are building a robust Internal Developer Platform (IDP) and creating the tools that enable our engineers to ship high-performance code at scale. If you love writing clean code and get equally excited about a perfectly orchestrated Kubernetes cluster or an elegant Terra grunt module, you’ll fit right in.
What you'll do: Architect & Build: Take full ownership of our infrastructure, from backend services to cloud resources. Empower Developers: Develop and maintain our homegrown IDP , ensuring a seamless "golden path" for engineering teams. Scale the Stack: Design and manage scalable, reliable environments using AWS, Argo CD, and Helm. Automate Everything: Build sophisticated CI/CD workflows using GitHub Actions and manage stateful infrastructure with Terra grunt. Innovate: Evaluate and integrate new CNCF tools and internal utilities to keep our platform at the cutting edge.

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.

We're looking for a System Architect to own the technical foundation end to end.

What you'll do

Own the architecture - a scalable, cloud-native system from whiteboard to production on a greenfield Azure environment you'll stand up yourself (Kubernetes-native, Everything-as-Code, GitOps, CI/CD from day one).
Work directly with our Chief AI Officer to translate complex decisioning methodologies into clean,self-service products a customer can run without a data-science team.
Lead the coding. Set the standards, make the hard calls, and write the core code yourself. You'll guide the engineers shoulder-to-shoulder - leadership by example, not by org chart, without managing them directly.
Stand up a fully agentic SDLC - building the product withAI coding agents end to end, so a small team ships like a large one.
Design the integration layer so the product plugs into each host's native agent and data surfaces - customers adopt it without leaving the tools they already use.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

We are seeking an AI Full-Stack Developer to build and scale automation solutions across internal company processes using AI, LLMs, and agent-based systems. This role focuses on delivering practical automation solutions, integrating AI capabilities with internal tools, and orchestrating workflows across multiple systems, with a dynamic range of tasks that offer both quick wins and complex challenges.

Responsibilities:

Build AI-driven automation solutions using LLMs, APIs, and agent frameworks.
Develop systems to automate document processing, data extraction, and operational workflows.
Design and implement multi-step automated workflows connecting AI models and internal services.
Integrate automation solutions with internal systems such as HR, finance, legal, dashboards, and ticketing tools.
Deliver and iterate practical automation solutions swiftly, from simple GPT/AI integrations to complex workflows.
Train and support internal teams on newly built or optimized systems for strong adoption and self-sufficiency.
Collaborate with Technical Team Lead, Product Manager, and IT teams on architecture and system integration.
Communicate clearly with internal stakeholders and external partners or vendors.

We're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent - but also informed.

We work with some of the most innovative teams in AI - from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.

The Role: DevOps Engineer
Managing Kubernetes clusters across multiple environments and regions

Owning infrastructure as code for all resources

Maintaining and improving CI/CD pipelines and GitOps-based deployments

Maintaining and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors

Building out monitoring, alerting, and observability

Debugging production issues across services

Managing cloud costs and capacity planning

Working closely with a small engineering team - you'd own infra, not a slice of it

Were looking for a Senior Backend Software Engineer to help develop our hyperscaling platform.
We have a lot of tasks on Golang and little less on Java and Python.

In this position, your responsibility will be to:
Developing fault-tolerant, reliable cloud services: cloud infrastructure, managed databases, managed Kubernetes, billing, monitoring systems, ML service and more.

We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.



Some directions we are currently working on, and which you can be a part of:

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We're an SRE team within DevTools, looking for someone ready to help maintain and grow our systems
Your responsibilities will include:

Improving services based on user feedback

Building fault-tolerant, self-healing architecture

Finding ways to speed up our systems and reduce user friction

Modifying well-known closed- and open-source solutions Supporting our users

we are looking for a Senior Site Reliability Engineer (SRE)
Your responsibilities will include:

Ensure fault-tolerance, scale, and uninterrupted operations for the service.
Use cutting-edge cloud technology to solve a variety of infrastructure problems.
Implement and improve CI/CD processes.
We expect you to have:

Solid experience with programming languages (like Go, Python, or C++);
Solid understanding of classic algorithms and data structures;
Commercial experience with and deep understanding of Unix systems and network technology;
Experience with systems for containerization and configuration management (Ansible, Salt, Terraform, Docker, K8s, Helm).

What youll do

Design and build backend services powering live voice conversations: low-latency, real-time, and reliable under load.

Develop the platform our customers use to create, test, and analyze their voice agents.

Integrate with telephony providers, CRMs, calendars, and the wider monday.com platform.

Turn research prototypes into production systems together with our AI scientists.

Own what you ship: architecture, code quality, observability, and performance.