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).
- Solid 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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