Job
Description
About The Opportunity A high-growth player in the Enterprise AI & Cloud Data Engineering sector, we design and ship production-grade large-language-model (LLM) and machine-learning solutions that power real-time analytics, intelligent search, and automated decision support for global customers. Our engineers translate cutting-edge GenAI research into reliable, secure, and low-latency services at scale—leveraging modern Azure tooling and open-source ML stacks to deliver business impact fast. Role & Responsibilities Own end-to-end GenAI/ML use-cases: architect, implement, and optimise scalable pipelines—data ingestion ➜ training ➜ MCP-based checkpointing ➜ inference. Develop Python back-end services that integrate LangChain/LangGraph workflows, FAISS vector search, and agentic tool-calling into cloud APIs. Collaborate with Data Scientists to refine classification & regression models, boosting throughput and sub-second latency for live traffic. Embed ML training & inference into Azure Data Factory / PySpark pipelines, automating retraining and monitoring drift. Define engineering-excellence playbooks (code style, CI/CD, observability) and mentor peers on best practices. Build reliability tooling—health checks, auto-scaling rules on AKS, and alerting dashboards—to maximise uptime and performance. Skills & Qualifications Must-Have Strong Python coding skills (back-end services, data pipelines) and basic PySpark. 1–3 yrs hands-on building LLM / GenAI applications with LangChain or LangGraph plus FAISS-backed retrieval. Solid grasp of core machine-learning algorithms (classification, regression) and production use with ScikitLearn. Experience deploying models via Azure Machine Learning, Data Lake / Blob Storage, and SQL/NoSQL back-ends. Familiarity with MCP-style model-checkpointing and agentic workflow patterns (tool invocation, planning-execution loops). Git-centred workflow, unit testing, and containerisation fundamentals. Preferred Exposure to Azure Kubernetes Service (AKS) or other orchestration for low-latency inference. Knowledge of prompt-engineering, chunking strategies, and evaluation for RAG pipelines. Experience automating monitoring, retraining, and A/B experiments in production. Skills: Python,Azure SQL,Relational Databases,RESTful APIs,Azure Cloud Services,ORMs,Asynchronous Programming,Multithreading,version control,debugging,ci/cd,problem-solving,kubernetes,docker,communication,teamwork Show more Show less
