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 4-6 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 Prometheus Grafana OpenTelemetry 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 Job Type Full time Experience AI 4 years Required Python 4 years Required Work Location In person

📌 Senior Ai Engineer (Tamil Nadu)
🏢 Xerago
📍 Tamil Nadu