דרושים » AI » Data Architect

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructuresomeone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacksits about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakesdesigned for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object storebacked data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

Required Software Engineer - Al Platform
About Us
We help modern, mid-size businesses transform the way they manage people, giving HR and managers all they need to connect, engage, develop, and retain top talent. Since 2015, weve achieved consecutive triple-digit year-over-year growth, all backed by our amazing team of Bobbers from across the globe, making us the choice HRIS of over ~4500 midsize and multinational companies and over 1 Milion users.
Our HR platform is intuitive, data-driven, and built for the way people work today: globally, remotely, and collaboratively.
What this role is really about
Were building our internal AI Platform as a foundation that enables safe, scalable, and cost‑effective AI across the company. Our mission is to support internal business units in becoming more productive and driving business growth. As a Software Engineer on Business Innovation (Internal), youll develop high‑leverage platform capabilities used by Product, Customer Success, Sales, Operations, Data, and IT. Youll work across the entire stack, from APIs and services to data and UI. transforming business requi
Build core AI platform services - Design and implement agent orchestration, prompt management, RAG, Connectors, and evaluation pipelines that power AI experiences across the company.
Develop complex agentic workflows - Develop a multi-step workflow that coordinates tools and services with proper observability, guardrails, and cost controls (using OpenAI Agent SDK, LangGraph, or a similar framework).
Build LLM evaluation and optimization systems -Develop evaluation harnesses, offline/online experiments, prompt-testing frameworks, and dashboards to balance quality, latency, and spend across all AI services.
Ship the internal AI portal - Build user-facing products that democratize AI access: prompt directories, agent designers, templates, approval workflows, and more.
Ensure security and compliance - Develop RBAC, data classification, PII redaction, audit logging, and policy enforcement.
Create robust integrations - Connect the platform with Salesforce, NetSuite, Slack, Snowflake, HRIS, and other critical business systems.
Establish operational excellence - Implement CI/CD, testing strategies, monitoring/tracing, and SLOs for high-reliability services.
Enable developers - build SDKs, CLIs, and templates that help teams create safe, scalable AI workflows. Lead technical discussions and write design documentation.

Architect and Evolve Data Infrastructure- Design and implement scalable data pipelines, Data Lakes, and Data Warehouses to support analytics, AI, and product intelligence.
Build and Deploy AI-Powered Systems- Operationalize AI/ML models- from prototyping to production-grade inference- ensuring performance, monitoring, and reliability.
Enable BI and Analytics- Collaborate with business and product teams to turn raw data into actionable insights, visualizations, and predictive metrics.
Ensure Data Quality and Governance- Establish standards for data collection, validation, lineage, and retention.
Leverage LLMs and AI APIs- Integrate modern large language models and external AI services for chatbots, insights generation, and automation use cases.
Collaborate Across Teams- Work closely with engineers, ML specialists, and product stakeholders to align data and AI architecture with business goals.

we are looking for a AI Engineer to join our R&D organization and help lead the development of AI-powered and data-driven capabilities in our Developer Productivity Platform.
In this role, you will transform data into actionable insights and intelligent models that help software teams understand, measure, and improve how they work. Youll work closely with product, data, and engineering teams to design and deliver AI and data science solutions that have real impact on developer productivity and business outcomes.

Key Responsibilities:
Design, develop, and deploy AI-driven features & data science and ML models to analyze code flows, software development processes, team behaviors, and performance metrics.
Build AI & data pipelines and analytical infrastructure that ensure data quality, accessibility, and freshness at scale.
Translate complex data into clear, actionable insights and code analysis that influence product direction and drive customer value.
Develop and maintain predictive, prescriptive, and diagnostic models to support use cases like delivery forecasting, productivity measurement, and team health analysis.
Partner with Product Managers, Data Engineers, and Backend Engineers to bring insights to life through AI & data-driven features and platform improvements.
Monitor and continuously improve model performance through experimentation, A/B testing, and user feedback loops.
Research and evaluate new AI/ML tools and methodologies to enhance the platforms intelligence and scalability.

In this role, youll own the engineering backbone that enables our data engineers & data scientists to experiment, innovate, and deploy production-ready models, ensuring our systems are robust, scalable, and future-proof.

What You'll Actually Do:

Lead and mentor a team of Data Engineers, fostering a culture of technical excellence, accountability, and collaboration.
Define and drive the teams technical roadmap, standards, and best practices for data modeling, performance, and reliability.
Design, build, and evolve scalable embedding pipelines and recommendation infrastructure, from algorithm tuning and training workflows to production deployment.
Manage and optimize vector db, ensuring efficient indexing strategies, search performance, and low-latency retrieval.
Lead the development of retrieval services leveraging Approximate Nearest Neighbor (ANN/KNN) techniques such as HNSW, IVF, and Product Quantization (PQ).
Ensure reliability, observability, and scalability across all real-time data systems powering our recommendation engine.
Collaborate with Data Science, Product, and Analytics to operationalize models, validate KPIs, and translate technical insights into actionable product improvements.

We are looking for an exceptional MLOps Team Lead to own, build, and scale the infrastructure and automation that powers Labs state-of-the-art Large Language Models (LLMs) and AI systems.
This is a technical leadership role that blends hands-on engineering with strategic vision. You will define MLOps best practices, build high-performance ML infrastructure, and lead a world-class team working at the intersection of AI research and production-grade ML systems.
You will work closely with LLM Algorithm Researchers, ML Engineers, and Data Scientists to enable fast, scalable, and reliable ML workflows covering everything from distributed training to real-time inference optimization.
If you have deep technical expertise, thrive in high-scale AI environments, and want to lead the next generation of MLOps, we want to hear from you.

As an AI Engineer on our Search Engine team, youll help build the state-of-the-art, powered-by-AI search. Youll design and operate high-performance services that transform user intent into fast, accurate, and reliable results. Your work sits at the foundation of how customers discover companies and contacts, delivering a seamless, intelligent search experience at scale.

What You'll Actually Do:

Build lightning-fast APIs serving recommendations, contact, and company data to millions of users daily.
Design event-driven systems that sync data across products and data lakes in real-time.
Develop agentic flows and integrate AI solutions that optimize user experience.
Optimize data retrieval, caching, and indexing for sub-second response times at scale.
Create fault-tolerant services with robust monitoring and CI/CD that just work.
Collaborate with Data Scientists and ML engineers to power AI-driven features and pipelines that customers use.

As a Senior LLM Applications Developer, youll design and integrate Large Language Models (LLMs), agentic AI, and AI agents into production at scale. Youll architect backend systems, optimize prompts, and lead technical decisions that define how AI powers our caregiving platform. Beyond coding, youll mentor the team and pioneer new agent-driven AI capabilities that drive our mission forward.



What Youll Do

Design and develop backend services for AI-driven applications
Architect and optimize prompts for LLMs in production
Integrate AI/LLM models into scalable, secure, cloud-based systems
Collaborate with AI, Product, and Data teams to deliver impactful features
Mentor developers and set best practices in backend engineering
Explore new AI models and tools, leading adoption into production

We are seeking a Data Engineering Lead to own and scale data platform infrastructure

Build our new data Lakehouse to support various product and business needs
Support cutting-edge AI Agents and cybersecurity use cases
Be part of the Data and AI Algorithms group
Collaborate closely with dev teams, AI/ML engineers, security researchers and business teams
Ensure the availability, reliability and quality of our data infrastructure
Help define best practices for data modeling and orchestration at scale

As an AI Architect, you will own the end-to-end design of our AI-driven protection stack, from agent telemetry to inference and enforcement. You will set the standards for secure and scalable AI architecture, influence product direction, and mentor senior engineers. If you want to lead the intersection of AI, security, and system design, this is where you want to be.

Key Responsibilities


Define the end-to-end AI architecture, from agent telemetry to inference and protection enforcement.
Design APIs and interfaces connecting agents, management, and GenAI services.
Partner with threat research and product teams to turn protection intent into ML-driven capabilities.
Establish best practices for AI safety, privacy, and secure design.
Review designs across all product teams (Windows, Mac, Backend, GenAI).
Mentor senior engineers and lead design reviews for high-impact systems.