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Harmony is the AI voice venture built inside monday.com. Our voice agents plan, reason, and act on real phone calls for businesses - answering, qualifying, booking, and resolving. As an Implementation Consultant you are the hands-on expert who turns a signed deal into a live, high-performing voice agent.
This is an individual-contributor role with real ownership: you run your own implementations, work directly with customers, and shape how our delivery practice works as we grow.
What youll do
Own implementation projects of varying scope and complexity end to end: discovery, requirements, configuration, testing, roll-out, training, and launch.
Translate customer goals into call flows, conversation scripts, escalation rules, and hand-off logic in the Harmony Agent Builder.
Configure integrations with the customers stack: CRM, telephony, calendars, and ticketing systems.
Test and tune agents against real call scenarios until quality, containment, and conversion targets are met.
Advise customers on voice AI best practices and help them expand to new use cases after launch.
Work closely with Sales, Customer Success, Product, and Engineering, feeding field learnings back into the product.
Build the playbooks, templates, and delivery methods that will let the implementation practice scale.

Harmony is the AI voice venture built inside monday.com. Our agents hold real phone conversations for businesses, and every one of those calls runs through systems we design and build ourselves: real-time audio pipelines, agent orchestration, telephony integrations, and the tools our customers use to build and monitor their agents.
We are a small team that ships fast. Engineers here own features end to end - from design through production - and work shoulder to shoulder with AI researchers turning models into product.
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.

Shape the developer-facing surfaces of our company- the CLI, dashboard, App Builder, and documentation. You will own full features from design to production across TypeScript, React, and Go.

Own the model plane end to end - vLLM inference, fine-tuning (GRPO/RL and LoRA), and the evaluation-and-promote pipeline that takes an owned model from checkpoint to production serving. We train and serve our own models on GPUs in our own cluster.

Build and scale the infrastructure layer behind every our company service - EKS orchestration, Pulumi IaC modules, Helm chart automation, and the observability stack that keeps the platform trustworthy.

Own the surfaces developers touch every day - the company CLI, our SDKs, the Electron desktop IDE, and the docs. Make the path from zero to a deployed app feel effortless.

Required Full Stack Developers
Join our development team in Ra'anana (hybrid).
We build complex SAAS solutions for cyber, medical, and more, creating applications from scratch.
:Your Day
Handle the entire development cycle, from planning to delivery.
Solve problems and collaborate with our customers.

Were looking for a Data Analyst with expertise in Tableau to join our growing data analytics team. This role is perfect for someone who thrives on turning data into actionable insights and enjoys working at the intersection of business and technology.
At Fetcherr, the Insights & Decision Science (IDS) team is where data meets storytelling. We take the hard work of our AI pricing engine and translate it into something airlines can actually see, trust, and act on - think rigorous statistical analysis with a human touch. If a number moves, we explain why. If a client has a question, we have the answer (and the chart to back it up).

We're looking for a Data Scientist to help us grow our advanced analytics team's capabilities. The ideal candidate is a data scientist with a relevant experience who is self-driven, motivated, independent and sharp
Design and operationalize causal-uplift measurement frameworks to quantify the incremental impact of pricing models
Lead data discovery and translate complex multidimensional data into actionable insights related to the pricing algorithms and their outcomes
Conduct research and experimentation to elaborate novel approaches to uplift measuring and a/b testing
Collaborate with revenue managers, data analysts, data engineers in order to encode airline business logic into modeling and technical pipelines
The Insights & Decision Science (IDS) team is where data meets storytelling. We take the hard work of our AI pricing engine and translate it into something airlines can actually see, trust, and act on - think rigorous statistical analysis with a human touch. If a number moves, we explain why. If a client has a question, we have the answer (and the chart to back it up).

We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.
Responsibilities:
Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.

We are looking for an Engineering Team Lead to lead the MLOps efforts - drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.
You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.
Responsibilities:
Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.
Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.
Stay hands-on: contribute to design and code, review work, and set engineering standards.
Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.
Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking an experienced Algorithm Team Lead - Optimization to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.
The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.
At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through an optimization engine that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.
You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.
Responsibilities:
Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve our data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.