Senior AI Engineer - Remote

📋 Description

• • Architect and deliver production-grade AI services that power mission-critical integrations for thousands of global enterprises, ensuring 99.9 % uptime and sub-second response times on a platform that processes billions of API calls per month.
• • Own the full lifecycle of AI feature development—from ideation and rapid prototyping with LLMs (OpenAI GPT-4, Anthropic Claude, AWS Bedrock, Azure OpenAI) to hardened, containerized micro-services running on Kubernetes clusters across multi-cloud environments.
• • Design and implement Retrieval-Augmented Generation (RAG) pipelines that combine vector search (Elasticsearch, Redis) with structured data from MySQL, InfluxDB, and Kafka streams, enabling customers to automate complex business logic using natural-language prompts.
• • Optimize inference latency and cost by fine-tuning open-source models (Llama-2, Falcon) with LoRA/QLoRA, deploying quantized versions on GPU-enabled nodes, and orchestrating autoscaling policies that react to real-time traffic spikes.
• • Build reusable Python/Java SDKs and LangChain/LlamaIndex abstractions that allow internal teams and external developers to embed AI capabilities—sentiment analysis, entity extraction, predictive mapping—into low-code integration flows without writing code.
• • Establish rigorous MLOps practices: versioned datasets, automated retraining pipelines, canary releases, A/B testing frameworks, and continuous drift monitoring to guarantee model performance and regulatory compliance (GDPR, SOC 2, HIPAA).
• • Collaborate cross-functionally with product managers to translate customer pain points into AI-driven solutions, delivering weekly demos, technical specs, and roadmap presentations that influence strategic direction and secure executive buy-in.
• • Champion code quality through peer reviews, unit/integration tests (>90 % coverage), and static analysis tools; enforce architectural standards that keep the 15-year-old Java/C++ monolith and new Go/Python services coherent and maintainable.
• • Mentor junior engineers via pair programming, brown-bag sessions, and guild meetings; create internal wikis and runbooks that democratize AI knowledge across the fully remote, 200-person engineering organization spanning 12 time zones.
• • Participate in a follow-the-sun on-call rotation, using Prometheus, Grafana, and PagerDuty to diagnose and resolve incidents within SLA; conduct blameless post-mortems that feed directly into backlog prioritization and reliability improvements.
• • Contribute to open-source communities (Apache Kafka, Kubernetes, LangChain) and represent Jitterbit at AI conferences, meetups, and webinars, amplifying our thought-leadership and attracting top-tier talent to the team.