Data&AIEngineer

Annapurna IT
Greater London, UK
20 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Software Applications Data Cleansing Python (Programming Language) Software Engineering Systems Integration Unstructured Data Enterprise Software Applications Retrieval-Augmented Generation Large Language Models Data Management
+1 more
Software Coding

Job description

Our client is seeking a pragmatic, hands-on Data & AI Engineer to design, build, and operate AI-powered applications and agents that solve real business problems. You’ll join our client’s team to enhance existing AI solutions, create new AI capabilities that improve workflows and decision-making, and work closely with business stakeholders and engineers to embed domain expertise into robust, production-grade AI systems.

Requirements

  • Python software development - Strong, production-level coding skills to build reliable, maintainable AI services.
  • End-to-end software delivery - Experience designing, building, testing, deploying, and operating production-grade systems.
  • AI/LLM application development - Hands-on experience creating AI or LLM-based applications used by real users, not just experimenting with consumer tools.
  • AI system lifecycle - Exposure to prototyping, data preparation, evaluation, deployment, monitoring, and continuous improvement of AI systems.
  • AI evaluation and metrics - Ability to design and run evaluations, and measure model/agent quality, reliability, safety, and business impact.
  • Working with multiple model providers - Understanding of different model families, trade-offs in latency, cost, context limits, and deployment constraints.
  • Prompt and agent design - Experience treating prompts and agent instructions as versioned, testable software artifacts.
  • Systems integration - Integrating AI with APIs, enterprise applications, data platforms, and both structured and unstructured data.
  • Advanced AI techniques - Familiarity with retrieval-augmented generation, tool calling, structured outputs, agent orchestration, and workflow automation.
  • Model adaptation foundations - Understanding when to use prompting, retrieval, fine-tuning, and how to prepare and evaluate data and models for post-training.
  • Business and data understanding - Ability to grasp complex workflows, rules, and processes, and translate them into AI-enabled solutions.

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