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.
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.
- Other 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.
Must have Skills : Python, FastAPI, LLM Application Frameworks, Vector Databases and Embeddings,
Skills Required
- Daily use of AI-assisted and agentic coding tools (e.g., Claude Code, Cursor, GitHub Copilot)
- Strong experience with Python
- Experience building REST APIs with FastAPI
- Grounding in NLP and machine learning for LLM systems
- Experience with LLM model APIs (OpenAI, Anthropic)
- Experience building, deploying, and securing MCP servers at scale
- Understanding of multi-agent systems
- Designing and implementing RAG systems, vector databases, semantic search, chunking strategies, embeddings
- Experience with prompt writing for various use cases
- Experience shipping generative solutions to production at scale
- Proficiency with server-side events, event-driven architectures, and messaging systems
- Solid understanding of backend security best practices (authentication, data protection)
- 2+ years developing and experimenting with LLMs
- 8+ years developing APIs with Python
- Experience with LLM frameworks (e.g., LangChain, LlamaIndex)
- Experience with LLM guardrails and monitoring/observability
- Experience building evaluation harnesses, regression suites, and LLM-as-judge systems
Nagarro Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nagarro and has not been reviewed or approved by Nagarro.
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Pay Growth & Progression — Compensation is at times described as competitive, with salary hikes and perks occurring on certain occasions. Better growth opportunities and compensation are also positioned as an advantage versus other service-based companies.
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Flexible Benefits — Work arrangements are framed around a “work-from-anywhere” mindset with flexitime and family-friendly working models. This flexibility appears to add meaningful value to the overall rewards package for many roles.
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Healthcare Strength — Medical, dental, and vision coverage are described as available for employees and dependents, alongside life insurance. Mental-health support is also included via an Employee Assistance Program (EAP).
Nagarro Insights
What We Do
Nagarro helps future-proof your business through a forward-thinking, fluidic, and CARING mindset. We excel at digital engineering and help our clients become human-centric, digital-first organizations, augmenting their ability to be responsive, efficient, intimate, creative, and sustainable. Today, we are 19,000 experts across 36 countries, forming a Nation of Nagarrians, ready to help our customers succeed.
