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Kela Technologies is a defense technology startup building next-generation command & control and data infrastructure systems for defense organizations. The Platform as a Service (PaaS) team delivers customer projects on the Kela platform and translates learnings into reusable products.
You will serve as a Data / ML Platform Engineer, bridging the data platform and customer-facing engineering functions. This hybrid role combines internal tooling development with direct customer engagement, including on-site and forward-deployed work when needed.
Key Responsibilities:
- Support customer algorithm teams across the full ML lifecycle: data annotation, training, evaluation, and observability, covering both platform infrastructure and hands-on project work
- Work directly with customer teams to understand their data and ML needs, unblock challenges, and translate insights into platform improvements
- Seed new data products from early concept through deployment and adaptation in customer projects
- Build and operate data and ML pipelines for multimodal sensor data (video, radar, RF, telemetry)
- Set up and maintain data environments, model onboarding flows, and evaluation workflows for customer research teams
- Partner with algorithms, product, and DevOps teams to establish data and MLOps standards across the company
The role is designed to grow toward either the data platform or customer-facing side based on your preference and team needs. You will be a core PaaS team member while collaborating closely with core software, algorithms, and DevOps teams.
Requirements:
- Minimum 3 years of experience in data engineering, MLOps, or backend engineering building production systems
- Strong Python proficiency with production-grade code and pipeline experience
- Strong SQL skills and solid data modeling fundamentals
- Hands-on experience with Kubernetes and containerized deployments
- Practical ML workflow experience: training, evaluation, experiment tracking, and model deployment
- Comfortable working directly with customers or technical stakeholders; able to translate unclear requests into working solutions
- Ability to work independently and move between infrastructure building and project delivery
- Demonstrated full-stack builder mentality through professional or personal projects
Preferred Qualifications:
- Algorithms background with ability to build and run model comparison evaluations (e.g., re-identification models)
- Experience with semantic layers (dbt Semantic Layer, Snowflake Semantic Views) or ontology technologies (e.g., Palantir)
- MLOps frameworks experience (MLflow, Kubeflow, Airflow orchestration)
- Data lake architecture knowledge (Iceberg, Delta) and query engines (Snowflake, Athena, BigQuery)
- ELT/ETL pipeline design experience (Airflow, dbt)
- Exposure to multimodal sensor data (EO/IR, radar, RF, geospatial, IMU, LiDAR)
- Background in defense, aerospace, or mission-critical systems
