Xerago - Senior AI Full Stack Engineer

Description

Roles & Responsibilities :

• Architect scalable AI solutions : Define end-to-end reference architectures (LLM/RAG, NLP, vision, agentic workflows) that move cleanly from DemoBytes/POC/ MVP / Demoable / Production.
• Own full-stack delivery : Build features across data/ML, backend APIs/services (FastAPI/Flask), and lightweight UIs (React/Next.js) for demoable, user-ready outputs.
• Rapid prototyping : Stand up POCs in days; harden validated solutions into MVP and production with incremental quality/security gates.
• MLOps & platformization : Implement CI/CD/CT for models, datasets, prompts; automate evals, canary/rollback, versioning, model/data drift monitoring, and experiment tracking (W&B/MLflow).
• Integration & interoperability : Embed AI into existing products and workflows via APIs, queues, SDKs, and webhooks with clear SLAs and observability.
• Operate what you build : Instrument services, track p95 latency/availability/cost, and drive continuous improvement post-launch.
• Mentor & uplift : Coach engineers on best practices (prompting, vector design, evals, latency/cost tuning, secure data handling).
• Release cadence : Maintain monthly demo releases and production releases every two months with ALM-driven governance.
• Ethical AI & compliance : Apply privacy-by-design, bias testing/mitigation, model cards, auditability, and data protection controls; ensure documentation in ALM.
• Trendwatching : Track state-of-the-art AI (models, toolchains, infra) and pragmatically incorporate breakthroughs into roadmaps.
  

Qualifications

• 46 years delivering AI/ML features to production with fast POC/MVP /Production cycles.
• Strong ML/DL fundamentals; hands-on with PyTorch and/or TensorFlow/Keras; LLMs (prompting, fine-tuning/LoRA), RAG patterns, and evaluation.
• Python proficiency; scikit-learn, spaCy/NLTK; Hugging Face (Transformers/Datasets/PEFT); familiarity with YOLO/FastAI (role-relevant).
• Backend engineering for production (FastAPI/Flask), auth, caching, testing; practical React/Next.js for demoable UIs.
• MLOps : Docker/Kubernetes, CI/CD (GitHub Actions/Azure DevOps/Jenkins), experiment tracking (Weights & Biases/MLflow), monitoring Data & storage : SQL/NoSQL (Postgres, Redis), object stores; vector DBs (FAISS/Milvus/pgvector) and retrieval design.
• Cloud : AWS/Azure/GCP with cost/latency/performance trade-off literacy.
• AI productivity tools (required) : Cursor, Windsurf, Claude, Copilot for accelerated prototyping, code gen/review, and prompt workflows.
• Effective communication; crisp documentation and governance in ALM.
• Working knowledge of ethical AI and data protection (PII handling, access controls, audit trails).