BuildxPartners
AI Software Architect / Senior AI Engineer (LLM & Agentic Systems)
- Compensation
- Not disclosed
- Location
- Seri Lingampally, India
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Master's 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.
Nice to have
- 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.
Full description
Position Overview
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.