Associate Staff Engineer(AI)

Remote from: China
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 22 May 2026
Apply before: 22 Jun 2026
Experience level: Midweight
Views / Applies: 24 / 7

Nagarro is a global digital engineering leader.

Actively Hiring

AI Summary

This role is for an Associate Staff Engineer specializing in Agentic AI, focusing on designing, building, and optimizing generative AI applications. The engineer will work on fine-tuning large language models, integrating them with enterprise systems, and ensuring scalable and reliable performance. The position requires strong Python skills, API development experience, and a deep understanding of AI/ML fundamentals. Ideal candidates have a background in computer science or AI and experience with LLM frameworks and cloud environments. The job offers an opportunity to collaborate with cross-functional teams to translate business requirements into AI solutions.

Job Complexity

Easy Hard

AI Insight The role requires advanced expertise in generative AI, LLM fine-tuning, and system integration, which are highly specialized skills. The combination of AI fundamentals, Python, API development, and cross-functional collaboration makes it challenging, but it is not the highest difficulty level as it does not require cutting-edge research or novel algorithm development.

Salary Analysis

AI Insight The salary for this role is not explicitly provided, but based on market data for senior AI engineer positions in the US, the estimated median is $150,000. This is competitive for the required skills in generative AI and LLM integration. The market range for similar roles is $120,000 to $200,000, depending on location and experience.

Key Skills

Generative AI Python LLM Fine-tuning API Development Agentic AI System Integration AI/ML Fundamentals Cloud AI Environments Data Pipelines Cross-functional Collaboration

Cover Letter Sample

I am writing to express my strong interest in the Associate Staff Engineer (AI) position. With a Master's degree in Computer Science and over 5 years of experience in AI and machine learning, I have developed deep expertise in generative AI, LLM fine-tuning, and system integration. My background includes building and deploying agentic AI applications that improved business processes by 30%.

In my previous role, I led the development of an AI-driven workflow automation tool that integrated multiple enterprise APIs and unstructured data sources. I am proficient in Python, API development, and modern LLM frameworks such as LangChain and Hugging Face. I have also collaborated closely with product teams to translate business requirements into scalable AI solutions.

I am particularly excited about this opportunity because it combines my passion for cutting-edge AI with the chance to work on agentic systems that can operate autonomously. Your focus on performance, scalability, and reliability aligns perfectly with my experience in optimizing AI pipelines for production environments.

I am confident that my technical skills and collaborative mindset would make me a valuable addition to your team. I look forward to the possibility of discussing how I can contribute to your AI initiatives.

Possible Interview Questions

Describe your experience with designing and deploying agentic AI applications. What challenges did you face and how did you overcome them?

I have designed and deployed several agentic AI applications, including a customer support chatbot that used LLMs to autonomously resolve tickets. One challenge was ensuring the agent could handle ambiguous queries; I implemented a confidence threshold and escalation mechanism. I also optimized the pipeline for latency by caching frequent responses and using asynchronous processing.

How do you approach fine-tuning a large language model for a specific business use case? Can you walk us through the process?

I start by collecting domain-specific data and cleaning it for quality. Then I choose a base model (e.g., GPT-3.5 or Llama) and use techniques like supervised fine-tuning with prompt engineering. I evaluate on a held-out set and iterate on hyperparameters. For example, I fine-tuned a model for legal document summarization, achieving a 15% improvement in ROUGE scores.

Explain a time when you had to integrate an LLM system with enterprise APIs and data sources. How did you ensure reliability and security?

I integrated a LLM-based recommendation engine with a CRM API and a database. I used API gateways for rate limiting and authentication, and implemented retry logic for failures. Data was encrypted in transit and at rest. I also set up monitoring to alert on latency spikes. This ensured 99.9% uptime and no data breaches.

What is your experience with Python and API development? Can you provide an example of a complex API you built?

I have extensive Python experience, including building RESTful APIs with FastAPI. One complex API was a multi-agent orchestration service that received user queries, routed them to different LLM agents, and aggregated responses. It handled concurrent requests with async I/O and included input validation and error handling.

How do you stay updated with the latest advancements in generative AI and LLMs? How do you decide which new technologies to adopt?

I follow key researchers and papers on arXiv, attend conferences like NeurIPS, and experiment with open-source models. I evaluate new technologies based on performance benchmarks, community support, and alignment with business needs. For example, I adopted LangChain after seeing its flexibility in chaining LLM calls.

Job Description

Job Summary:

We are looking for a Senior Agentic AI Engineer to design, build, and optimize high-quality agentic AI applications. This role focuses on developing intelligent AI systems powered by generative AI, improving agentic engineering workflows, and integrating large language models with enterprise systems and data platforms. The ideal candidate combines strong technical depth in AI fundamentals and Python with hands-on experience in API development and system integration.

Key Responsibilities:

Design and develop high-quality agentic AI applications using generative AI technologies. Optimize agentic AI engineering processes for performance, scalability, and reliability. Fine-tune and enhance large language model-based systems for specific business use cases. Build and manage integrations between LLM systems, APIs, and enterprise applications. Integrate structured and unstructured data sources to support AI-driven workflows. Collaborate with cross-functional teams to translate product and business requirements into AI solutions. Ensure solutions follow best practices in software engineering, testing, security, and deployment. Monitor, evaluate, and continuously improve AI system performance and response quality.

Required Skills:

Strong expertise in Generative AI. Strong foundation in AI/ML fundamentals. Strong programming skills in Python. Capable in API development and integration. Experience building and deploying agentic AI applications. Knowledge of LLM fine-tuning, orchestration, and system integration. Ability to work with data integration pipelines and connected systems. Good to Have Product design experience. Understanding of user-centric AI solution design. Experience working in cross-functional product development environments.

Preferred Qualification:

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field. Proven experience in developing and optimizing AI-driven applications. Familiarity with modern LLM frameworks, orchestration tools, and cloud-based AI environments. Strong problem-solving skills and ability to work in a fast-paced environment.

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