Nagarro
Senior Staff Engineer (LLM)

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Job Description
Role Overview:
The LLM Engineer will join an existing development team to build and ship LLM-powered features in a complex, production application used at scale. This is a hands-on, full-stack role spanning backend services, APIs, and the LLM systems (retrieval, agents, evaluation) that power them. You are expected to work as an "agentic engineer" using AI coding tools and autonomous agents to write code, automate workflows, and optimize how the team delivers. You will collaborate with global teams across multiple time zones and own features end to end.
In this role, you will:
- Bring senior-level Python and LLM engineering expertise to the team.
- Execute both planning and hands-on technical work independently.
- Collaborate effectively with Product Owners and other stakeholders to solve complex problems. Work cross-functionally to deliver impactful solutions across teams.
- Continuously develop your technical expertise and stay current with new technologies.
- Bring curiosity and drive to expand your skills and knowledge.
- Use a data-driven approach to solve technical challenges and make informed decisions.
- Apply systems-level thinking that integrates data science and engineering principles.
- Take full ownership of the features and projects you work on, delivering high-quality solutions independently.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Must-Have Skills:
- Hands-on, daily use of AI-assisted and agentic coding tools (e.g., Claude Code, Cursor, GitHub Copilot, autonomous coding agents) to write and refactor code, automate workflows, and optimize engineering processes.
- Strong experience with Python, particularly in building REST APIs using frameworks like FastAPI.
- Grounding in NLP and machine learning as they relate to building LLM systems.
- Strong experience working with key LLM models APIs (e.g., OpenAI, Anthropic).
- Experience building, deploying, and securing MCP servers at scale.
- Understanding of multi-agent systems and their applications in complex problem-solving scenarios.
- Designing and implementing RAG systems end to end: vector databases, semantic search, retrieval quality, and chunking strategy.
- Experience with prompt writing for various use cases.
- Experience with generative solutions released to prod, at scale, beyond POCs.
- Proficiency with server-side events, event-driven architectures, and messaging systems.
- Strong critical thinking and systems thinking skills, with experience debugging, optimizing, and making sound engineering decisions across complex backend systems, not just solving isolated problems.
- Solid understanding of security best practices for backend systems, including authentication and data protection.


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Other Qualifications:
Qualifications
- 2+ years of experience developing and experimenting with LLMs.
- 8+ years of experience developing APIs with Python.
Nice-to-Have Skills:
- Experience with LLM guardrails.
- Experience with LLM Frameworks (e.g., LangChain, LlamaIndex).
- Experience with LLM monitoring and observability.
- Experience developing AI/ML technologies within large and business critical applications.
- Building evaluation into LLM systems: eval harnesses, regression suites, LLM-as-judge, and offline/online quality metrics.
Additional Information:
Must-have Skills: Python, FastAPI, LLM Application Frameworks, Vector Databases and Embeddings
Service Region: Others
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