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MWDN
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MWDN is a global IT outstaffing company with 23+ years of experience that connects exceptional tech talent with leading companies across Israel, the USA, Great Britain, and Western Europe. We offer opportunities to work on international products in a stable and professional environment.
Here’s what you can expect when you join MWDN:
Why choose us? MWDN is ranked among the top 5 IT employers in our region according to DOU. We take pride in our transparency and strong commitment to our team. Curious to learn more? See what our employees say about working with us on DOU.
Domain: Venture Capital and Private Equity Principals
Client Location: Israel
Company size: 10-50
Founded in: 2016
We’re looking for a senior engineer who can build AI native products on top of reliable, secure, high quality data systems. Someone who has experience shipping production level software for high trust workflows, understands data pipelines and data quality deeply, can deploy safe agentic systems, and knows how to design robust architecture that scales.
This role is for someone who has shipped production software at scale, understands data pipelines and data quality deeply, and knows how to deploy AI systems that are useful, observable, and safe.
You’ll work across the full stack: data ingestion, transformation, storage, APIs, product surfaces, AI workflows, agentic systems, and observability.
You have strong production engineering judgment and can move quickly without compromising reliability, data integrity, and security:
● Strong experience with TypeScript, React, SQL
● Deep backend architecture, API, data modeling, and production debugging skills
● Significant experience with data pipelines, integrations, or data-heavy systems
● Experience building systems that handle sensitive, regulated, and high trust data
● Production experience shipping robust AI/LLM-powered features
● Strong understanding of evals, hallucinations, malformed outputs, retrieval quality, latency, cost, model drift, and provider risk
● Good judgment about when to use an LLM, when to use retrieval, when to improve the data foundation, and when deterministic code is the better answer
● Excellent written and verbal communication in English
● You know how to build AI systems that are useful without being reckless.
● You understand prompt injection, permission boundaries, data exfiltration risk, secrets handling, audit logs, and human in the loop review. You treat model context as untrusted input and design agent systems with scoped tools, explicit permissions, logging, evals, and graceful failure modes.
● Security is non-negotiable. You should be comfortable with access control, Postgres RLS, PII handling, secrets management, audit trails, least privilege, secure integrations, and safe use of third-party APIs.
● You have built sturdy data pipelines customer facing products rely on
● You care about data correctness before AI or dashboards depend on it
● You can trace a bad number across ingestion, transformation, database, API, UI, and AI context
● You think naturally in failure modes: stale data, duplicate events, schema drift, partial writes, bad joins, expired tokens, provider outages, and malformed model outputs
● You have worked in high trust data environments such as finance, investment, legal, healthcare, or enterprise saas
● You have experience with Supabase, Postgres RLS, Deno, Modal, embeddings, vector search, transcription, GPU workloads, or similar infrastructure
● You have worked with data validation, reconciliation, lineage, observability, or audit systems
● You have experience with AI evals, AI observability, agent testing, or red-teaming AI systems
You will design and ship production-grade systems that combine software engineering, data infrastructure, and AI, including:
● Building secure, scalable full-stack product features
● Designing data models, APIs, integrations, and backend architecture
● Creating reliable data pipelines with strong validation and observability
● Shipping high value high trust AI/LLM-powered features
● Building safe agentic workflows with scoped tools, permissions, logging, evals, and failure handling
● Debugging issues across ingestion, transformation, database, API, UI, and AI context
● Designing systems with security, privacy, auditability, and observability
✅ Intro call with a Recruiter — ✅ Intro with the client — ✅ Test Task — ✅ Final client interview— ✅ Offer
Originally posted on Himalayas
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