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AI Engineer Technical consultant-AI Integration
Job Location:
Monthly Salary:
Posted:
8 September 2026 (17 hours ago)
Posted:
Application Deadline:
6 December 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
total 8-12 yrs Relevant 4 yrs
Develop and evaluate AI/GenAI/AgenticAI prototypes using tools like Copilot Studio AI Foundry and Copilot Analyst Agent Mosiac AI Genie AgentBricks MLflow with a focus on quick wins and enterprise integration.
Build and tune Retrieval-Augmented Generation (RAG) systems including embedding model selection prompt engineering and traceable evaluation.
Design and deploy basic AI agents using frameworks such as LangChain AutoGen and smolagents.
Communicate complex AI concepts clearly to business stakeholders and cross-functional teams.
Collaborate on Shell-E platform enhancements and work within its current limitations.
Deploy models and applications using Azure OpenAI Azure AI Foundry Databricks Mosaic Gateway and Docker.
Follow DevOps best practices including CI/CD pipelines testing linting and GitHub workflows.
Write modular reusable code using OOP design patterns in Python (Pydantic PyTorch etc.).
Operate in agile teams and contribute to sprint planning reviews and retrospectives.
Deliver hands on GenAI/AgenticAI systems used directly by commercial teams within Trading & Supply taking solutions from prototype to production
Apply engineering skills (emphasis on Databricks) and research skills across experimentation rapid prototyping and iterative delivery. Someone who puts emphasis on reproducibility and open source manages large-scale text and structured datasets on Databricks.
Build AI capability manage stakeholders and communicate effectively to ensure alignment between business needs and AI solutions and a quick understanding of commercial operations that happen in T&S
Design and run evaluation and testing frameworks for GenAI systems including benchmarking reproducibility checks and structured model assessments
Build solutions using Databricks infrastructure Genie MLflow (deployment and tracing and evaluations) LangChain and LangGraph and integrate them into scalable AI workflows and architectures
Contribute to system planning architectural design and structured testing to ensure long term reliability performance and maintainability
Preferably also someone who can set the building blocks and lead building out the backlog
Develop and evaluate AI/GenAI/AgenticAI prototypes using tools like Copilot Studio AI Foundry and Copilot Analyst Agent Mosiac AI Genie AgentBricks MLflow with a focus on quick wins and enterprise integration.
Build and tune Retrieval-Augmented Generation (RAG) systems including embedding model selection prompt engineering and traceable evaluation.
Design and deploy basic AI agents using frameworks such as LangChain AutoGen and smolagents.
Communicate complex AI concepts clearly to business stakeholders and cross-functional teams.
Collaborate on Shell-E platform enhancements and work within its current limitations.
Deploy models and applications using Azure OpenAI Azure AI Foundry Databricks Mosaic Gateway and Docker.
Follow DevOps best practices including CI/CD pipelines testing linting and GitHub workflows.
Write modular reusable code using OOP design patterns in Python (Pydantic PyTorch etc.).
Operate in agile teams and contribute to sprint planning reviews and retrospectives.
Deliver hands on GenAI/AgenticAI systems used directly by commercial teams within Trading & Supply taking solutions from prototype to production
Apply engineering skills (emphasis on Databricks) and research skills across experimentation rapid prototyping and iterative delivery. Someone who puts emphasis on reproducibility and open source manages large-scale text and structured datasets on Databricks.
Build AI capability manage stakeholders and communicate effectively to ensure alignment between business needs and AI solutions and a quick understanding of commercial operations that happen in T&S
Design and run evaluation and testing frameworks for GenAI systems including benchmarking reproducibility checks and structured model assessments
Build solutions using Databricks infrastructure Genie MLflow (deployment and tracing and evaluations) LangChain and LangGraph and integrate them into scalable AI workflows and architectures
Contribute to system planning architectural design and structured testing to ensure long term reliability performance and maintainability
Preferably also someone who can set the building blocks and lead building out the backlog
Required Experience:
Contract
