Senior DevOps Engineer

** Required Country Location: Costa Rica, Peru, Argentina, Brazil, Columbia, South Africa, Mexico, or Panama.**

** This is full time work at 40 hours per week** Overview**:
Our client seeks a motivated **Senior DevOps Engineer, Data & AI** to join their team in building a groundbreaking, modular platform from the ground up. This platform 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 DevOps leader with strong expertise in data engineering, distributed systems, and applied AI, who thrives on automation, scalability, and production-grade deployments across hybrid and cloud environments.

**Key Responsibilities**:
**Platform Automation & Infrastructure**
- Architect, automate, and manage infrastructure for **data ingestion, contextualization, and visualization modules** (Data, Access, & Agents).
- Build CI/CD pipelines for sensor collection agents across heterogeneous systems (Windows, Linux, macOS, mobile, IoT).
- Implement and automate **real-time ingestion pipelines** using **Apache Kafka, Apache NiFi, Redis Streams, or AWS Kinesis**.

**Database & Data Layer Engineering**
- Deploy, scale, and optimize multi-modal databases:

- ** Time-series**: MongoDB, InfluxDB, TimescaleDB, or AWS Timestream
- ** Graph**: Neo4j (Cypher, APOC, graph schema design)
- ** Vector**: Qdrant, FAISS, Pinecone, or Weaviate
- Automate deployment and monitoring of a **Database Access Layer (DBAL)** to unify queries across multiple database engines.
- Experiment with or extend **Model Context Protocol (MCP)** or similar standards for cross-database and multi-agent interoperability.

**Data Streaming & Integration**
- Engineer **low-latency pipelines** for event streams (syslog, telemetry, keystrokes, IoT feeds, cloud service logs).
- Collaborate with frontend engineers to integrate **visual mapping UIs** with scalable back-end pipelines.

**Optimization, Reliability & Scalability**
- Optimize system and database performance using **down-sampling, partitioning, and caching techniques**.
- Design solutions for **horizontal scaling and containerized deployment** (Docker, Kubernetes, OpenShift).

**Collaboration & Leadership**
- Partner with compliance, security, and business stakeholders to ensure systems meet regulatory and operational requirements.
- Conduct architecture reviews, lead DevOps best practices, and mentor junior engineers on automation, scalability, and observability.

**Required Skills & Experience**:

- ** Programming**: Strong proficiency in Python and Node.js (C++ a plus).
- ** Streaming**: Proven hands-on experience with **Kafka, NiFi, Redis Streams, or AWS Kinesis**.
- ** Databases**:

- Time-series: MongoDB, InfluxDB, TimescaleDB, or AWS Timestream
- Graph: Neo4j (Cypher, APOC)
- 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 standardized APIs for multi-database access.
- ** Cloud & Infrastructure**: Hands-on with **AWS, Azure, or GCP**, plus containerization and orchestration (**Docker, Kubernetes, OpenShift**).
- ** DevOps Expertise**: Deep understanding of **CI/CD pipelines, IaC (Terraform/Ansible), monitoring/observability, distributed systems, and microservices security**.
- ** Problem Solving**: Strong debugging skills, automation mindset, and ability to balance speed, scalability, and compliance in production systems.

**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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