{"id":2524184,"url":"https://alion.io/job/kela-data-ml-platform-engineer","title":"Data / ML Platform Engineer","company":{"id":1971233,"name":"Kela","domain":"kela.io","url":"https://alion.io/company/kela-io","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Kubernetes","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"BigQuery","optional":true},{"name":"dbt","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-10-14T07:52:00Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-14T19:00:50Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Description\nKela is a defense technology startup pioneering next-generation command & control and data infrastructure systems. We empower defense organizations with robust, scalable, and secure platforms that integrate diverse sensors and AI to deliver real-time battlefield intelligence. Backed by leading investors, Kela combines Israeli innovation with global mission impact.\nThe Platform as a Service team is Kela's customer-facing engineering function: it delivers customer projects on the Kela platform and turns what it learns into reusable products.\nWe are looking for a Data / ML Platform Engineer to sit between our data platform and our customer-facing work. You will build the data and ML infrastructure and work directly with customer algorithm teams to drive their projects, including on-site and forward-deployed when the work needs it. You are not only a builder of internal tooling, and you are not only a customer-facing integrator. You do both, with a strong data foundation.\nThe role is hybrid by design. It is expected to grow toward either the data platform side or the customer-facing side, based on your preference and what the team needs. You will also seed new data products from an early idea, then carry them into real customer projects.\nYou will be a core member Kela's PaaS team, while working closely with our core software, algorithms, and DevOps teams.\nResponsibilities:\nSupport customer algorithm teams across the full ML lifecycle: data annotation, training, evaluation, and observability, covering both the platform infrastructure and the hands-on project work\nWork directly with customer teams to understand their data and ML needs, unblock them, and turn what you learn into platform improvements\nSeed new data products from scratch, and take them forward by deploying and adapting them in customer projects\nBuild and operate data and ML pipelines for multimodal sensor data (video, radar, RF, telemetry)\nSet up and maintain the data environments, model onboarding flows, and evaluation workflows that customer-side research teams rely on\nPartner with algorithms, product, and DevOps teams to establish data and MLOps standards across the company\nRequirements:\nAt least 3 years of experience in data engineering, MLOps, or backend engineering, building production systems\nStrong proficiency in Python, with experience writing production-grade code and pipelines\nStrong SQL skills and solid data modeling fundamentals\nHands-on experience with Kubernetes and containerized deployments\nExperience with ML workflows in practice: training, evaluation, experiment tracking, and model deployment\nComfortable working directly with customers or technical stakeholders, and able to turn an unclear request into a working solution\nAbility to work independently, and to move between building infrastructure and delivering a project\nDemonstrated full-stack “builder” mentality through professional or personal projects\nPreferred Qualifications:\nAn algorithms background: able to build and run evaluations that compare models (for example, re-identification models)\nExperience with a semantic layer (dbt Semantic Layer, Snowflake Semantic Views) or ontology technologies (for example, Palantir)\nExperience with MLOps frameworks (for example, MLflow, Kubeflow, Airflow orchestration)\nExperience with data lake architectures (Iceberg, Delta) and query engines (Snowflake, Athena, BigQuery)\nFamiliarity with ELT/ETL pipeline design (Airflow, dbt)\nExposure to multimodal sensor data (EO/IR, radar, RF, geospatial, IMU, LiDAR)\nBackground in defense, aerospace, or other mission-critical systems","description_format":"text","description_chars":3613,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Defense AI"],"lifecycle":[{"event":"open","at":"2026-10-14T11:56:32Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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