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.