Senior AI / Generative AI Engineer

N-Labs; Hyderabad; On-site; INR 2,400,000 - 4,000,000; Full time

Job summary

N-Labs in Hyderabad, India is seeking a Senior AI / Generative AI Engineer with around 4 years of experience. You will own architecture, implementation, deployment, and optimization of complex AI workflows, including Agentic AI, LLMs, and RAG systems.

You will design multi-agent orchestration, tool-calling pipelines, and scalable backend services using Python, FastAPI/Flask, and AWS. Collaboration with stakeholders to automate business processes will be key.

Qualifications

• 4 years of AI or software engineering experience with Generative AI.
• Strong Python and SQL expertise with deployment experience.
• Hands-on with LLM-powered applications and multi-agent workflows.

Responsibilities

• Architect and develop production-grade Agentic AI and Generative AI solutions.
• Design multi-agent systems for task orchestration and tool calling.
• Create advanced RAG architectures for knowledge grounding and semantic search.
• Own LLM workflow architecture from requirements to deployment.
• Build reliable tool-calling and structured-output pipelines for business processes.
• Develop backend services and APIs using Python and FastAPI/Flask.
• Design event-driven AI workflows using AWS services.
• Establish evaluation, validation, regression testing and monitoring for non-deterministic AI systems.
• Optimize AI systems for accuracy, latency, scalability, reliability, and cost.
• Provide guidance and mentoring to junior AI engineers.
• Collaborate with stakeholders to identify automation opportunities.

Skills

Python

SQL

LLM deployment

Agentic AI

LangChain

LangGraph

RAG architectures

REST API

AWS

Prompt design

Tools

LangChain

LangGraph

FastAPI/Flask

REST APIs

AWS

Job description

We are looking for a Senior AI / Generative AI Engineer with around 4 years of experience designing and delivering production-grade Agentic AI, LLM, and RAG solutions. The candidate should be capable of owning architecture, implementation, evaluation, deployment, and optimization of complex AI workflows.

Key Responsibilities

• Architect and develop production-grade Agentic AI and Generative AI solutions.
• Design multi-agent systems involving task orchestration, routing, tool calling, and agent collaboration.
• Design advanced RAG architectures for enterprise knowledge grounding and semantic search.
• Own LLM workflow architecture from requirements and prompt design through backend integration and cloud deployment.
• Build reliable tool-calling and structured-output pipelines for complex business processes.
• Develop high-quality backend services and APIs using Python and FastAPI/Flask.
• Design event-driven and distributed AI workflows using AWS Lambda, S3, SQS, and EC2.
• Establish evaluation, validation, regression testing, and monitoring approaches for non-deterministic AI systems.
• Optimize AI systems for accuracy, latency, scalability, reliability, and cost.
• Provide technical guidance and mentoring to junior AI engineers.
• Work with stakeholders to identify automation opportunities and translate them into scalable AI solutions.

Mandatory Skills

• 4 years of relevant experience in AI, Data Science, Generative AI, or software engineering.
• Strong Python and SQL expertise.
• Proven experience building and deploying LLM-powered applications.
• Strong expertise in Agentic AI, multi-agent workflows, and LLM orchestration.
• Strong LangChain and LangGraph experience.
• Advanced RAG, semantic search, embeddings, and vector database experience.
• Strong FastAPI/Flask and REST API development skills.
• Hands-on AWS experience with Lambda, S3, SQS, and EC2.
• Experience designing evaluation and regression strategies for LLM applications.
• Ability to independently own technical design and production delivery.

Preferred Background

Experience with OpenAI, Anthropic, Llama, Groq, or comparable foundation models; enterprise automation, procurement, analytics, or document-intelligence use cases; PostgreSQL, MySQL, MongoDB, or MSSQL; building AI-driven dashboards or business applications; and mentoring engineers and driving technical decisions. Candidates should demonstrate practical implementation capability rather than purely theoretical knowledge.