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.
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