Technical Manager

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, and 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.