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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI/ML engineering and Azure application development, with strong proficiency in Python, .NET, and SQL. Capable of building and deploying production-grade AI solutions while providing operational support and enhancing existing systems.
Highest-signal resume keywords
Python ProficiencyAzure Application DevelopmentAI/ML EngineeringSQL CommandMLOps Familiarity
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML DevelopmentAPI DevelopmentApplication DevelopmentData ExtractionModel TrainingPrompt EngineeringREST API ExposureDocker KnowledgeKubernetes ExperienceSQL Proficiency
Soft Skills
Analytical ThinkingClear CommunicationProactive Problem-SolvingIndependent WorkTeam Collaboration
Tools & Technologies
AzureTensorFlowPyTorchScikit-learnMLflowKubeflowFastAPIFlaskApache AirflowGit
Industry Keywords
Generative AIMachine Learning PipelinesCloud-Native AI ServicesAgent-Building FrameworksVector Databases
Tech Stack
Tools & technologiesAirflowApacheAzureCloudDockerFlaskGRPCKubernetesPythonPyTorchScikit-LearnSQLTensorflow.NET
About the role
Key responsibilities & impact- Design, develop, deploy and enhance production-grade AI solutions, including intelligent agents, generative AI applications and machine learning pipelines
- Own the ongoing enhancement and operational support of AI agent solutions, including defect resolution, deployments, known limitations, backlog items and future improvements
- Provide BAU support and enhancement delivery for production APIs on Azure, including incident investigation, root-cause analysis, code or configuration changes, testing and deployment coordination
- Provide Azure platform operational support across App Service, Azure SQL Database, Storage Accounts, managed identities, application settings, and related configuration and access requests
- Administer and support Azure SFTP, including user and directory creation and maintenance, permissions, approved access changes, and connectivity or path troubleshooting
- Support deployment and release processes, including environment configuration, pre-checks, post-deployment validation, rollback, and production support coordination
- Monitor application and platform health using App Service logs and Application Insights
- Perform structured root-cause analysis and implement corrective and preventive actions for recurring issues
- Apply strong software engineering practices and collaborate with cross-functional stakeholders and dependent teams to translate requirements into reliable, maintainable solutions and measurable outcomes
- Partner with brands globally and key functional areas to deliver technology that enables business vision and strategy
- Work with data platforms and tools to improve decision-making across the business
Requirements
What you’ll need- 1–3 years of relevant experience in AI/ML engineering, Azure application development, or a closely related role
- Strong proficiency in Python for AI/ML development, scripting, API development and automation
- Development experience with .NET and C# in the Azure ecosystem
- Solid command of SQL for data extraction, transformation, analysis and pipeline support
- Hands-on experience building applications with large language models, including prompt engineering, RAG architectures, embeddings and fine-tuning
- Experience with at least one agent-building framework such as Google ADK, LangChain or LangGraph, including tool use, memory management and multi-step agent orchestration
- Working experience with Azure deployment, compute, storage and cloud-native AI/ML services
- Practical knowledge of building and training models using TensorFlow, PyTorch or Scikit-learn
- Experience with vector databases such as Pinecone, Weaviate, ChromaDB, Qdrant or FAISS for similarity search and RAG pipelines
- Familiarity with MLOps and CI/CD tools such as MLflow, Kubeflow or Weights & Biases for experiment tracking, model versioning and deployment automation
- Working knowledge of Docker and Kubernetes for packaging and orchestrating AI workloads
- Experience building and exposing AI services through REST or gRPC APIs using FastAPI, Flask or similar frameworks
- Familiarity with Apache Airflow, Prefect or equivalent workflow orchestration tools
- Proficiency with Git and collaborative development workflows
- Strong analytical thinking, clear communication, the ability to work independently and across teams, and a proactive problem-solving mindset
Benefits
Comp & perks- Competitive salary
- Discretionary bonus
- Generous holiday allowance
- Hybrid and flexible working
- Employee transportation
- Pension and gratuity contributions
- Wellbeing support
- Medical insurance cover
- Personal accident insurance cover
- Term life insurance cover