Sr. Manager Data Engineer (Data Intelligence)

Job Summary

Lead the technical design of enterprise AI/ML and generative AI solutions, setting engineering standards and mentoring the AI squad to deliver production-grade models, deployment pipelines, and GenAI orchestration across the Groups Alia platform.

Responsibilities

• Lead the technical design and architecture of enterprise AI/ML solutions, model deployment pipelines, and generative AI orchestration on the companys enterprise GenAI platform (Alia) and other cloud AI platforms.
• Drive the design and delivery of agentic AI workflows and inference API integration, spanning real-time and batch inference services, across enterprise systems.
• Evaluate, select, and implement AI tools and services, including LLM providers, agent frameworks, and AI developer tooling, and drive their adoption across the squad.
• Set AI/ML engineering standards, reusable patterns, and best practices for model development, experiment tracking, deployment, and monitoring across the squad.
• Architect scalable model training and MLOps pipelines across cloud AI platforms (e.g. Azure AI, Google Vertex AI, AWS SageMaker), including feature stores, model versioning, and CI/CD for models, ensuring production reliability, cost observability, and incident runbooks for model services.
• Lead advanced prompt engineering and LLM interaction design, including retrievalaugmented generation and vector database integration, for generative AI use cases.
• Establish model performance metrics (accuracy, F1, AUC, precision, recall) and LLM evaluation metrics (e.g. relevance, groundedness, hallucination rate), drift detection (data and prediction drift), and retraining strategies and triggers.
• Own responsible AI standards, including fairness, bias detection and mitigation, model cards, and decision logging for auditability and governance.
• Link model outputs to business KPIs and value creation, such as time saved, conversion, or revenue impact, partnering with product and business teams.
• Mentor and provide technical guidance to AI/ML engineers, reviewing designs and code and growing squad capability.

What you'll need to succeed

• Minimum 7 years of experience in AI/ML engineering, data science, or machine learning, including leading technical design and mentoring engineers.
• Deep expertise in Python and machine learning frameworks (TensorFlow, PyTorch, Scikit-learn), with strong data handling (Pandas, NumPy) and experiment tracking (MLflow).
• Strong SQL skills for data exploration, feature engineering, and working with cloud data warehouses (e.g. BigQuery, Snowflake).
• Strong experience with generative AI and large language models, including prompt engineering, retrieval-augmented generation, agentic workflows, and vector databases.
• Hands-on experience implementing AI solutions with LLM APIs and models (e.g. OpenAI, Anthropic Claude, Google Gemini, open-source models), orchestration frameworks (e.g. LangChain, LangGraph, LlamaIndex), and supporting tools for evaluation, guardrails, and LLM observability (e.g. LangSmith, Langfuse).
• Hands-on experience architecting MLOps pipelines (feature stores, model versioning, CI/CD, monitoring) across cloud AI platforms (Azure AI, GCP), with REST and GraphQL API integration.
• Strong grasp of the full model development lifecycle: feature engineering, hyperparameter tuning, evaluation, deployment, drift detection, and retraining.
• Experience embedding responsible AI practices (fairness, bias detection, model cards) and linking model outcomes to measurable business value.
• Hands-on experience with AI coding assistants (e.g. Claude Code, GitHub Copilot, Cursor) for AI-assisted development.

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