*required country location: costa rica, peru, argentina, brazil, columbia, south africa, mexico, or panama.
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* this is full time work at 40 hours per week*
*overview*
our client seeks a motivated *senior developer, data & ai* to join their team in developing a groundbreaking, modular platform built from the ground up.
our client digitizes and contextualizes *multi-modal sensor data* from both digital and physical environments into specialized *time-series, graph, and vector databases*, powering real-time analytics, compliance, and ai-driven context mapping.
this role is ideal for a creative problem-solver who thrives at the intersection of *data engineering, distributed systems, and applied ai*.
*key responsibilities*
*platform design & development*
- architect, develop, and deploy *core modules* (data, access, & agent's) for end-to-end data ingestion, contextualization, and visualization.
- design and code *sensor collection agents* across heterogeneous systems (windows, linux, macos, mobile, iot).
- implement *real-time ingestion pipelines* using technologies like *apache kafka, apache nifi, redis streams, or aws kinesis*.
- persist and query multi-modal data across *time-series (mongodb, influxdb, timescaledb), graph (neo4j), and vector databases (qdrant, faiss, pinecone, or weaviate)*.
*api & data access layer*
- build secure, scalable *restful and graphql apis* for exposing platform data models, sensor configuration, and reporting.
- implement a *unified database access layer (dbal)* to abstract query logic across multiple databases.
- experiment with or extend *model context protocol (mcp)* or a similar standardized data interchange for multi-db, multi-agent interoperability.
*system integration & data streaming*
- develop *low-latency data pipelines* for transporting and transforming event streams (syslog, telemetry, keystrokes, iot feeds, cloud service logs).
- collaborate with frontend engineers to connect *access (visual mapping ui)* with back-end pipelines.
*optimization & scalability*
- optimize *database query performance* using down-sampling, partitioning, and caching techniques.
- design solutions for *horizontal scaling and containerized deployment (docker, kubernetes, openshift)*.
*collaboration & mentoring*
- work directly with *compliance officers, security analysts, and business process owners* to refine data models for regulatory and operational needs.
- conduct code reviews, mentor junior developers, and promote best practices across the team.
*required skills & experience*
- * programming*: strong proficiency in *node.js and python* (c++ a plus).
- * streaming*: hands-on experience with *kafka, nifi, redis streams, or aws kinesis*.
- * databases*:
- time-series: mongodb, influxdb, timescaledb, or aws timestream
- graph: neo4j (cypher, apoc, graph schema design)
- vector: qdrant, faiss, pinecone, or weaviate
- * ai/agents*: experience with—or strong interest in—*agentic ai frameworks*, multi-agent orchestration, and context-aware data processing.
- * data interchange*: familiarity with *mcp-like protocols* or interest in defining standardized apis for cross-database access.
- * cloud/infra*: aws, azure, or gcp with containerization (docker, kubernetes).
- * software engineering*: strong grasp of *algorithms, distributed systems, microservice design, and api security*.
- * problem solving*: strong debugging skills, creative mindset, and ability to balance speed with scalability.
*preferred skills*
- machine learning/nlp integration into multi-modal pipelines.
- ci/cd automation and devops practices.
- knowledge of *enterprise integration patterns, event-driven systems, and zero-trust security models*.
- experience with compliance frameworks (nerc cip, fedramp, gdpr, sox).
*qualifications*
- bachelor's degree in computer science, engineering, or related field (or equivalent hands-on experience).
- * 5+ years professional software development* with data-intensive or ai-driven systems.
- proven experience *designing, deploying, and scaling modular platforms* in production.
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