דרושים » דאטה » Senior Data & AI Engineer

In this role on our AI team, you'll focus on agentic workflow automation. You'll design, build, and deploy reliable AI agents that automate complex HR, payroll, and payment workflows, handling decision-making, document intelligence, and complex tasks that typically require human judgment.

What You'll Do:
Design and build agent-based systems that automate document processing, business insights, and complex enterprise workflows.
Build observability, evaluation, and feedback loops for agent behavior to improve reliability, accuracy, and trust in production.
Own the technical architecture and engineering standards for agentic systems.
Build or manage data pipelines to process large volumes of documents and unstructured data.
Collaborate with domain experts to identify high-value automation targets and deliver end-to-end solutions.
Stay current on agent frameworks, LLM capabilities and limitations, and apply emerging patterns pragmatically in production.

we are looking for a Senior Data Engineer to join our Data Platform team. We are seeking a hands-on individual with experience in building and scaling modern data infrastructure, passionate about exploring AI technologies and applying them to enhance and evolve our data systems. This role is ideal for someone focused on data systems, performance, automation, and reliability, who will design, build, and maintain core components of our data pipelines, storage, monitoring, and observability stack, with a strong focus on improving workflows and system capabilities, while embracing the Shift4 way.

Responsibilities:

Build and maintain scalable, reliable, and high-performance data infrastructure across cloud and on-prem environments
Partner with engineering, analytics, and observability teams to support data flow from ingestion to consumption
Develop infrastructure as code and automation tools to improve deployment, monitoring, and recovery
Help troubleshoot performance bottlenecks and reliability issues in large-scale data pipelines
Implement data quality processes aligned with security standards to ensure accuracy, consistency, and compliance with financial regulations and PCI requirements.
Continuously improve the performance and efficiency of the data platform with proactive tuning and benchmarking
Proactively explore and apply AI tools to enhance efficiency across the data engineering lifecycle, while improving accessibility and usability of data solutions for teams across the organization

A hands-on engineering role focused on building production-grade AI/ML systems and the automation infrastructure that supports them - driving AI adoption into developer workflows, internal tooling, and domain-specific applications across the organization.

As a member of the AI Infrastructure & Applications team, you will lead the design, development, and production deployment of AI/ML-powered systems alongside the automation infrastructure and developer platforms that support them.
You will architect intelligent, scalable solutions used across the organization - driving AI adoption into developer and automation workflows, internal tooling, and domain-specific applications - while also building and maintaining the automation frameworks and infrastructure those systems depend on.
The systems you build are expected to be production-grade, reliable, observable, and continuously improving.

Responsibilities
Architect and ship end-to-end AI-powered applications and pipelines, from prototype to production.
Build agentic systems, RAG pipelines, and tool-use patterns that integrate LLMs into real workflows.
Define and own AI quality metrics (accuracy, groundedness, hallucination rate, task completion) and integrate them into CI/CD release gates.
Design evaluation frameworks for non-deterministic systems: offline evals, human-in-the-loop review, and automated regression suites.
Harness AI/LLMs to extend and enhance existing automation infrastructure, improving system performance and operational efficiency.
Build scalable automation frameworks, APIs, and tooling used across the organization.
Collaborate with engineering, CI, and domain teams to address automation needs across hardware, software, and cloud.
Distill requirements from a large, diverse user base into generic, reusable, maintainable solutions.
Implement monitoring, drift detection, and structured feedback pipelines for continuous improvement.
Apply rigorous engineering discipline - test design, release criteria, rollback strategies - to AI-native deployments.
Partner with product, design, and domain experts to define use cases, acceptance criteria, and rollout plans.

This is a high-ownership, builder-first Sr. Software Engineer role. You will design, build, and ship AI-integrated data systems from concept through production - owning outcomes end-to-end, including deployment, monitoring, cost, and business impact.

We are seeking a candidate who views AI tooling as a fundamental force multiplier in their daily engineering process. This position is central to our transition into an AI-native function, requiring an individual capable of making decisive, pragmatic architectural choices on reversible matters to maintain momentum. We need an experienced builder of production-grade, data-centric systems who is obsessed with delivering customer value and possesses a deep, curious enthusiasm for the transformative potential of AI.

What You Will Build
AI-Native Systems Development. Design, build, and own scalable data and ML pipelines, backend services, and AI-powered capabilities that are part of the platform's production decision-making layer. AI and ML components are runtime dependencies in this role - not research projects or experiments. Candidates will have strong back end and data engineering skills to thrive in this space.
Daily Shipping. Decompose complex work into safely mergeable increments and ship them daily. Treat large, multi-day pull requests as a risk to momentum. Use feature flags, canary releases, and rollback architecture to manage risk through isolation - not through avoidance.
AI-Augmented Engineering Workflow. Leverage AI-assisted development tooling (code generation, automated testing, architecture prototyping) as a core workflow multiplier. Evaluate and experiment with emerging AI tools and frameworks with direct hands-on engagement. Bring technical depth to AI fluency - architecture and capability tradeoffs, not surface-level awareness.
End-to-End Ownership. Own your work from design through production deployment, operational monitoring, and business impact measurement. Accountability extends beyond the feature to CI/CD pipeline health, observability, cost efficiency, and domain-level outcomes.
Architectural Decision-Making. Make pragmatic, timely architectural choices that balance modern AI and data technologies with reliability, cost, and delivery speed. Distinguish reversible vs. irreversible decisions and move forward without waiting for consensus on the former. Document decisions in lightweight ADRs and own the outcomes.
Cross-Functional Collaboration. Partner with product, design, infrastructure, and GTM teams to translate customer and business needs into technical solutions. Operate with business awareness - understand how your systems impact revenue, customer outcomes, and strategic priorities.

We are looking for a Principal Software Engineer at the intersection of AI/ML and security. You will drive the architecture and delivery of autonomous agentic experiences that investigate, triage, remediate, and harden customer environments using LLMs, knowledge graphs, and the Security Graph (MSG).

This is a high-impact IC role reporting to the Exposure Management engineering leadership. You will influence strategy, mentor senior engineers, and ship production systems used by the largest enterprises in the world.

Responsibilities

Architect & Build Agentic Security Experiences
Design and implement Blue (investigate/triage), Green (remediate/harden), and Red (attack simulation) agents operating across customer environments.
Define agent architecture: planning, tool use (MCP skills), memory, context retrieval, and human-in-the-loop controls.
Build orchestration over the Security Graph, correlating exposure signals across cloud, device, identity, data, and AI.

Drive AI-Native Platform Design
Architect skill, knowledge, context, and memory layers for agentic security systems.
Design MCP-based skill interfaces for customization and extensibility.
Define how LLMs interact safely and accurately with structured security data (vulnerabilities, misconfigurations, attack paths, graph relationships).

Shape AI-for-Security Strategy
Partner with PMs and domain leads to define where AI autonomy vs. human involvement is needed.
Evaluate and integrate foundation models (Azure OpenAI, fine-tuned models) for tasks such as risk scoring, remediation planning, and blast radius analysis.
Stay ahead of industry solutions (Wiz AI-APP, CrowdStrike Charlotte AI, Palo Alto XSIAM).

Technical Leadership & Influence
Set technical direction across multiple teams (20-30 engineers).
Drive architecture decisions, design reviews, and engineering excellence.
Mentor senior engineers (L63-L65) and grow the AI-for-security discipline.
Represent the team across us (Security Copilot, MSG, Azure AI, Research).

In this role on our AI team, you'll build and scale core AI systems that power our products. You'll work across agentic document intelligence, autonomous agents, compliance AI, and ML-powered insights, prototyping quickly, building robust evaluations, and shipping production-grade AI with real business impact.

What You'll Do:
Build and ship AI/ML solutions using LLMs, agents, RAG, and document understanding models, alongside classic ML.
Prototype quickly, validate feasibility, and turn strong POCs into production systems.
Evaluate models and architectures, apply testing and guardrails to improve agent and service reliability.
Research and apply emerging techniques: multimodal/document AI, agentic frameworks, synthetic data generation, and new architectural approaches.
Work cross-functionally with product, R&D, and compliance teams to deliver end-to-end solutions.
Contribute to scalable, secure architecture and engineering best practices for AI delivery.

we are looking for a Mobile SDK Engineer.
What youll do:
Develop and maintain mobile SDK features used by developers worldwide
Design and implement reliable and performant SDK components across multiple mobile platforms and operating system versions
Take ownership of end-to-end quality, including design, implementation, testing, and production reliability
Debug and resolve complex issues across different devices, OS versions, and runtime environments
Improve SDK performance, stability, and reliability at scale
Develop and maintain SDK wrappers and integrations for cross-platform frameworks such as Unity, React Native, Flutter, and other mobile environments
Work with modern AI-driven engineering tools, automation systems, and internal developer platforms
Collaborate with engineers across mobile, backend, frontend, and platform teams
Participate in code reviews and contribute to engineering best practices

Were looking for a Data Engineer who wants to own the full pipeline: from raw event ingestion through distributed processing and into the serving layer that powers our clients dashboards and APIs. This isnt internal tooling - youll be building the actual product surface that advertisers and marketers rely on to understand whats working on the web.
As AI reshapes how data is processed, queried, and served, youll also have the opportunity to integrate AI and LLM-based capabilities into our data workflows - from intelligent data validation and anomaly detection to AI-powered analytics interfaces that make our platform smarter for clients.
What youll do:
Build & Own End-to-End Pipelines: Design, build, and optimize data pipelines that process billions of web events (sessions, in-app events, conversions) with strict freshness and accuracy SLAs. Youll work across the entire attribution data stack - from ingestion and distributed processing through to the serving layer.
Ensure Data Integrity: Own the correctness of complex attribution logic - deduplication, organic vs. non-organic classification, primary attribution resolution, and cross-platform user stitching across massive, partitioned datasets.
Performance Engineering: Optimize scalable distributed processes to meet strict client SLAs. Tune jobs, queries, and pipeline scheduling for maximum throughput and minimum latency.
Ship Client-Facing Features: Partner with Product, R&D, and client-facing teams to turn raw web signals into production-grade, queryable data products that advertisers interact with directly.
Leverage AI in Data Workflows: Explore and integrate AI/ML capabilities into the data platform - from automated data-quality checks and anomaly detection to LLM-powered analytics tools that enhance how clients interact with their data.

Build Modular AI Capabilities: Design and implement self-contained "AI Skills" that connect to the Platforms core data layers.
Ship End-to-End Autonomous Agents: Build agents that don't just "think," but act. You will deploy agents that handle the heavy lifting of the security lifecycle.

Bridge the Gap to Non-Technical Users: Create the abstractions that allow our customer success teams and client stakeholders to trigger or chain these AI workflows.

Own the AI Infrastructure & Evaluation: Maintain and contribute to the environment where these agents are born, ensuring they are grounded in truth.

Play a part in shaping the future of human-computer interaction. As an MLOps Engineer, you will be the backbone of the machine learning infrastructure that powers our speech, audio, and conversational AI teams - ensuring their models are trained on the best possible data.
You will bridge the gap between research, data science, and engineering, owning the full ML lifecycle from large-scale data pipelines and distributed GPU training through to low-latency, high-fidelity inference and optimization. You'll partner closely with Audio ML Engineers, Speech ML Engineers, and ML Data Scientists to remove friction across their workflows and accelerate the path from research to product.

The MLOps Engineer will drive end-to-end quality and operational excellence across data ingestion, model training, deployment pipelines, and MLOps tooling for our speech and audio ML platforms. This hire will build, deploy, and optimize production-grade systems with a strong emphasis on scalable, GPU-accelerated infrastructure. You will own the training infrastructure that powers distributed and self-supervised model training on HPC and Slurm-managed clusters, as well as the inference pipelines that bring low-latency, high-fidelity audio and speech models to production. You will establish standard methodologies for model integration, deployment, monitoring, and reproducibility using CI/CD principles.

Responsibilities
Design, build, and operate large-scale data pipelines for proprietary audio and speech datasets - supporting curation, quality monitoring, and validation at scale alongside our ML Data Science team.
Partner closely with Audio ML Engineers, Speech ML Engineers, ML Data Scientists, and product teams to define metrics, gather requirements, and bring new capabilities to life.
Build and operate distributed GPU training workflows, including job scheduling and resource management on Slurm-managed HPC clusters, for both supervised and self-supervised methods.
Optimize model inference for low latency and high-fidelity streaming across serving environments, including optimization for Apple silicon.
Design and maintain automated pipelines for model training, evaluation, versioning, and deployment, with special attention to speech, audio, and signal-processing workflows.
Identify and resolve bottlenecks in ML and data workflows, improving system reliability, latency, and throughput at scale.