Ai software engineering manager Work, Jobs in Wingate, Dur (with Salaries)

AI Software Engineering Manager
Accenture —Newcastle upon Tyne
- Full-time
- Master's degree
- Team management
- APIs
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Xplor —Newcastle upon Tyne
- Full-time
- Remote
- Computer Science
- Engineering
- English
Quick apply
Principal Software Engineer Backend Technologies, Platform (UK Remote)
Turnitin, LLC —Newcastle upon Tyne
- Full-time
- Remote
- Mentoring
- AI
- Annual leave
Quick apply
Product Marketing Manager, Trust, Privacy and AI (UK Remote)
Turnitin, LLC —Newcastle upon Tyne
- Full-time
- Remote
- Product management
- AI
- Communication skills
- Annual leave
Quick apply
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Software Engineering Manager
MHR INTERNATIONAL UK LIMITED —Newcastle upon Tyne
- Team management
- Mentoring
- Recruiting
- Employee discount
- Referral programme
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Sage —Newcastle upon Tyne
- Hybrid work
- Spanish
- Product management
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Ocado Retail —Sunderland
- Full-time
- Hybrid work
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- Annual leave
Product Manager, Regulatory Reporting & Filing
Sage —Newcastle upon Tyne
- Hybrid work
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- Analysis skills
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AI Software Engineering Manager

Accenture -
Newcastle upon Tyne

Apply Now

Job details

Full-time

Qualifications

CI/CD
Law
Client engagement
Cost control
Master's degree
CI/CD pipelines
Team management
Terraform
Back-end development
APIs
Solution architecture
AI
Software development or architecture experience
Infrastructure provisioning automation
Terraform provisioning
Debugging
SDLC

Full job description

You build the solutions that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent solution in production, you have owned the evaluation harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI solutions across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements to maximise efficiencies across the software delivery lifecycle (SDLC).

This sits at the heart of the AI engineering talent market — demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.

Key Responsibilities

Architect and govern production-grade agentic solutions at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability
Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable
Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models
Own LLMOps at programme scale: evaluation strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems
Lead client engineering engagements at senior level — analyse the SDLC to identify AI/agentic opportunities and use cases, facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams
Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp-up time on new client engagements
Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme-level AI impact in business terms to senior client stakeholders

Extensive software engineering experience in production environments
Hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable
Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level
Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs
RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering
LLMOps fundamentals: eval harness design, prompt versioning, and production observability
Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)
Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience
Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure
People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations

#LI-EU

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ai software engineering manager jobs in wingate, dur

AI Software Engineering Manager

Quality Engineer

Principal Software Engineer Backend Technologies, Platform (UK Remote)

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Product Manager, Digital and Real Time Tax Reporting

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