AI Architect

Weekday AI (YC W21); Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Weekday AI in Hyderabad is seeking an AI Software Architect / Senior AI Engineer to design and deliver production-grade AI solutions powered by Large Language Models. You will own architecture, build RAG-based apps, and orchestrate agentic workflows with tool calls and evaluation frameworks.

The ideal candidate has 10+ years of software engineering experience, strong Python skills, and proven delivery of production AI products.

Qualifications

• Masters degree in CS, AI, ML, or related field.
• 10+ years of software engineering with 5+ years in LLM-based solutions.
• Experience delivering production-grade AI applications using LLMs.
• Strong Python and cloud-native development fundamentals.

Responsibilities

• Own the architecture of AI-powered services and their integration with backend systems.
• Design, build, and maintain production-grade LLM applications using modern frameworks.
• Lead design reviews and drive architectural decisions across data pipelines and cloud infra.
• Architect agentic workflows with tool use, function calling, and memory management.
• Establish evaluation frameworks for AI quality, latency, cost, and business impact.
• Implement AI observability with Langfuse, LangSmith, and OpenTelemetry.
• Design RAG architectures using vector DBs and document pipelines.
• Collaborate with engineers, data scientists, product managers, and domain experts.

Skills

LLM architectures

Python development

AI orchestration frameworks

RAG

GCP

APIs design

Education

Master's degree in Computer Science, AI, ML, Software Eng or related

Tools

Docker

Kubernetes

LangGraph

LangChain

LlamaIndex

CrewAI

Job description

Job Description:

This role is for one of Weekday’s clients
Salary range: Rs 4000000 - Rs 7000000 (ie INR 40 - 70 LPA)

Min Experience: 10+ years
Location: Hyderabad
JobType: full-time

We are looking for an AI Software Architect / Senior AI Engineer who is a self-starter and thrives on designing and delivering production-grade AI solutions powered by Large Language Models (LLMs). This role requires deep expertise in architecting, building, and operating complex AI systems, including Retrieval-Augmented Generation (RAG), agentic workflows, tool calling, evaluation frameworks, and observability platforms.

The ideal candidate combines strong software engineering fundamentals with demonstrated experience delivering real-world AI products. Beyond experimentation, this individual must be capable of designing scalable, reliable, and cost-effective LLM-powered solutions that operate successfully in production environments. They should possess a strong understanding of modern AI architecture patterns, prompt engineering, retrieval systems, agent orchestration, and AI observability.

Key Responsibilities

• Own the architecture of AI-powered services and their integration with backend, mobile, and web applications.
• Design, build, and maintain production-grade LLM applications using modern AI frameworks and orchestration platforms.
• Lead technical design reviews and drive architectural decisions across AI services, backend systems, data pipelines, and cloud infrastructure.
• Architect agentic workflows involving tool use, function calling, multi-agent systems, planning, memory management, and reasoning chains.
• Establish evaluation frameworks to measure AI quality, reliability, latency, cost, and business impact.
• Implement AI observability and monitoring using platforms such as Langfuse, LangSmith, OpenTelemetry, and related tooling.
• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, document pipelines, and retrieval optimization techniques.
• Collaborate with software engineers, data scientists, product managers, and domain experts to translate business requirements into AI solutions.
• Optimize prompts, retrieval strategies, model selection, and system architecture for accuracy, reliability, performance, and cost efficiency.
• Design scalable APIs and services to expose AI capabilities across internal and external applications.
• Define AI engineering standards, best practices, and governance processes across the organization.
• Provide technical leadership and mentorship to engineers working on AI initiatives.
• Leverage cloud platforms such as Google Cloud Platform (GCP) to deploy and scale AI services.
• Document AI architectures, workflows, evaluation methodologies, and operational procedures.

Qualifications

• Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or related field.
• 10+ years of software engineering experience with at least 5 years focused on LLM-based solutions and generative AI systems.
• Demonstrated experience designing and deploying complex production-grade AI applications using Large Language Models.
• Extensive experience with AI orchestration frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, or equivalent technologies.
• Hands-on experience implementing AI observability and evaluation frameworks using Langfuse, LangSmith, or similar platforms.
• Proven expertise with Retrieval-Augmented Generation (RAG) architectures and vector database technologies.
• Strong understanding of modern LLM architectures, prompting strategies, context management, embeddings, and retrieval techniques.
• Strong Python development experience and software engineering fundamentals.
• Experience designing scalable APIs and cloud-native architectures.
• Ability to evaluate architectural trade-offs involving model performance, latency, reliability, maintainability, and cost.
• Experience deploying AI solutions in production environments using Docker, Kubernetes, and cloud platforms.
• Strong understanding of structured and unstructured data processing pipelines.
• Familiarity with modern database technologies including PostgreSQL, vector databases, and document stores.
• Excellent communication, leadership, and mentoring skills and ability to collaborate effectively with cross-functional teams.

Preferred Skills

• Experience building agentic systems involving tool use, planning, memory, and multi-agent orchestration and skills.
• Experience with model evaluation, benchmarking, AI testing frameworks, and automated quality assessment.
• Experience working with multiple commercial and open-source models including OpenAI, Anthropic, Gemini, Llama, and Mistral.
• Familiarity with fine-tuning, synthetic data generation, and model optimization techniques.
• Familiarity with developing and training machine learning models.
• Experience supporting AI products in regulated, privacy-sensitive, or high-availability environments.
• Experience integrating AI capabilities into mobile and web applications.
• Familiarity with modern software delivery practices including DevOps, CI/CD, and Agile development methodologies.

Must-have skills

• RAG
• LLM
• GCP

Good-to-have skills

• Python
• architecture