AI Engineer

Role Overview:

We develop and operate a highly data-intensive platform offering analytics, machine learning and agentic AI on cloud environments. As part of continued investment, we are building a cutting-edge AI-native decision-system.

As an Agentic AI Developer, you will be responsible for designing and developing advanced agentic AI solutions capable of autonomous planning, reasoning, and task execution. developing and integrating Retrieval-Augmented Generation (RAG) pipelines to enhance agents' reasoning capabilities with external knowledge sources, multi-agent communication protocols to enable seamless collaboration and coordination among agents. Collaboration will be key, as you work cross-functionally with Data Scientists, ML Engineers, and Product Teams to deliver robust, end-to-end AI solutions

You will work at the intersection of cutting-edge AI, Data Science and modern software engineering, implementing multi-agent frameworks and orchestrating interactions

Key Responsibilities:

• Design and implement Agentic AI architectures for enterprise workflows.
• Integrate Generative AI capabilities (LLMs, multimodal models) into client solutions.
• Deliver end-to-end AI solutions from ideation to production deployment.
• Build, fine-tune, and evaluate LLM-based Q&A models using frameworks like LangChain, HuggingFace Transformers, or OpenAI API.
• Design prompt templates and implement retrieval strategies to increase answer precision and factuality.
• Collaborate with product managers to translate user requirements into technical features.
• Participate in error analysis, iterative model improvement, and performance tuning.
• Engage with clients to identify high-value AI use cases and define business benefits.
• Conduct workshops and assessments to align AI strategies with organisational goals.
• Provide thought leadership on AI adoption and emerging trends.
• Develop reusable frameworks and accelerators for Agentic AI and GenAI.
• Ensure compliance with AI ethics, security, and governance standards.
• Stay on top of industry developments in Agentic AI, autonomous agents, and LLM ecosystems.
• Orchestrate complex multi-agent workflows to handle tasks requiring planning, reasoning, and tool use.
• Extend Microsoft 365 Copilot by building custom plugins and declarative agents within Microsoft Copilot Studio to surface enterprise data in Teams and Office apps.
• Operationalize AI solutions using Microsoft AI Foundry for model catalog management, Prompt Flow evaluation, and lifecycle governance.
• Architect scalable deployment patterns for agents using Azure Container Apps or Azure Functions, ensuring low-latency responses and cost-effective scaling.

Platforms and interfaces

• Own data flows, APIs, services, model-serving surfaces, front-end and desktop application surfaces, continuous integration and continuous delivery (CI/CD), and demo hardening.
• Build the systems that make quantitative work feel polished, reliable, and enterprise-ready for expert users and client stakeholders.

Agent-assisted systems

• Own the agentic harness layer — evaluation frameworks, reviewer loops, control-plane behavior, orchestration, and tool integration — that applications and MCPs wrap around.
• Design opinionated harnesses that expose through MCP or similar integration patterns without overfitting to one vendor or one moment in the tooling market.

Requirements:

• Strong experience in Agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI).
• Hands-on expertise with Generative AI (LLMs, prompt engineering, fine-tuning).
• Proficiency in Python and familiarity with deep learning/NLP libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers).
• Experience with building Q&A systems and retrieval-augmented generation pipelines.
• Knowledge of vector databases or semantic search concepts.
• Familiarity with cloud AI platforms (eg: Azure AI Foundry).
• Knowledge of MLOps practices and deployment pipelines.
• Experience with Git, collaborative development workflows, and cloud infrastructure.
• Proficiency in deploying AI workloads to Azure Container Apps (ACA), Azure Kubernetes Service (AKS), Databricks Apps or serverless functions (Azure Functions) for event-driven agent triggers.
• Experience implementing RAG using Azure AI Search (vector, semantic, and hybrid search) or Databricks One.

Qualifications:

• Bachelor's degree in computer science, engineering, mathematics, physics, economics, or a related field.
• Minimum 3 years of experience in technical delivery roles, with experience in moving products, internal tools, or workflow systems beyond proof-of-concept stage.
• Minimum 3 years of hands-on experience in one or more of the following areas: backend services, APIs and integrations, full-stack delivery, data pipelines, model-serving or machine learning workflows, or agentic orchestration systems.
• Strong coding ability in Python plus one complementary engineering surface such as TypeScript or JavaScript, front-end delivery, cloud or platform engineering, or infrastructure automation.
• Sound engineering judgment around enterprise hardening and evaluation, including experience with several of the following: authentication, role-based access control (RBAC), observability, security, release discipline, regression testing, or experiment frameworks for AI, machine learning, or agentic workflows.

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