Job Description

Role Description We are seeking a skilled Technical Lead to Lead the end-to-end design and delivery of an AI-powered solution --combining NLP, anomaly detection, GenAI/RAG, and rule engines on Cloud platform. Own architecture, technical roadmap, and production reliability while guiding a cross-functional team (ML, Data Eng, Backend, DevOps, QA).Key responsibilities Define reference architecture (ingestion ? lakehouse ? features/vectors ? models ? APIs/UX); make build/buy decisions. Select, train, and operationalize NLP, anomaly/fraud models, and GenAI/RAG components; establish human-in-the-loop. Implement experiment tracking, model registry, CI/CD for models, automated evaluation, drift monitoring, rollback. Design retrieval pipelines (chunking, embeddings, vector namespaces), guardrails (prompt policies, allow-lists, PII redaction), and citation-based responses. Oversee feature store, labelling strategy, and high-quality gold datasets; enforce DQ rules and lineage. Right-size SKUs; caching/batching; cost/per-token dashboards; SLOs for latency/throughput. Break down epics/stories, estimate and sequence work, unblock the team, run technical design reviews. Translate business policy into rule + ML ensembles, present options, risks, and trade-offs. Establish testing pyramid (unit, data, model evals, e2e), performance targets, observability dashboards. Produce design docs, runbooks, SOPs; mentor engineers; uplift coding and review standards.

Skills requirements 10+ years of software development experience, with at least 5 years leading AI/ML projects. Supervised/unsupervised modelling, anomaly detection, NLP (extraction, classification, NER), OCR pipelines; evaluation design (precision/recall, ROC/PR). LangChain/LangGraph; embeddings; vector databases / Azure Cognitive Search (vector); prompt engineering & safety patterns. MLflow, model registry, online/offline evals, data/version management; CI/CD for models. Delta Lake/Lakehouse (bronze/silver/gold), Azure Data Lake Gen2, Databricks/ADF, schema/versioning, Great Expectations/Deequ. Python (FastAPI), eventing (Event Grid/Service Bus), containerization (AKS/Azure Container Apps), APIM. Cognitive Search, Azure OpenAI, Key Vault, Monitor/App Insights, Purview, Cosmos DB/Azure SQL. Technical decision-making, cross-team coordination, stakeholder communication, mentoring. Preferred skills Certifications in AI/ML disciplines. Hands-on experience with explainable AI and AI governance. Familiarity with regulatory compliance standards for financial data (e.g., SOX, GDPR). Qualifications Bachelor's Degree in Computer Science or related science field or equivalent.