Associate Software Architect - Agentic AI Solutions

We are looking for an experienced Associate Software Architect - Agentic AI with strong expertise in software architecture, backend engineering, Python development, and emerging AI technologies.

The ideal candidate will be responsible for designing, architecting, developing, and deploying scalable, intelligent, and production-ready Agentic AI solutions.

This role requires a professional who can combine architectural vision with hands-on coding expertise and lead the development of autonomous AI systems powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent architectures.

The candidate should have a strong understanding of distributed systems, microservices, cloud-native applications, and AI engineering, along with the ability to translate complex business requirements into scalable technical solutions.

Key Responsibilities :

- Define and drive the architecture of scalable, secure, and high-performance AI-powered applications.

- Design end-to-end software architectures for Agentic AI platforms and intelligent automation solutions.

- Develop architectural blueprints, technical specifications, design patterns, and implementation strategies.

- Design distributed systems, microservices, APIs, and cloud-native applications.

- Ensure system scalability, reliability, maintainability, security, and performance.

- Evaluate emerging technologies and recommend appropriate tools, frameworks, and architectural approaches.

- Architect and develop autonomous AI agents capable of reasoning, planning, decision-making, and executing complex tasks.

- Design and implement multi-agent systems with effective communication, orchestration, and coordination mechanisms.

- Develop AI agents using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies.

- Implement agent workflows involving tool calling, function calling, memory management, and contextual reasoning.

- Build intelligent AI solutions that can independently execute multi-step workflows with appropriate human oversight.

- Optimize agent performance, reliability, accuracy, and response quality.

- Work extensively with LLMs such as OpenAI GPT, Anthropic Claude, Meta Llama, or other open-source and commercial models.

- Design and implement LLM-powered applications for enterprise use cases.

- Develop prompt engineering strategies, context management, and model integration techniques.

- Implement LLM orchestration, model selection, fine-tuning, and inference optimization where required.

- Evaluate model performance, response accuracy, latency, and cost efficiency.

- Implement responsible AI practices, guardrails, and output validation mechanisms.

- Design and implement scalable RAG architectures for enterprise AI applications.

- Develop document ingestion, preprocessing, chunking, embedding generation, and retrieval pipelines.

- Integrate vector databases such as Pinecone, Milvus, Weaviate, ChromaDB, or FAISS.

- Implement semantic search, hybrid search, reranking, and contextual retrieval techniques.

- Optimize retrieval accuracy, relevance, and response quality.

- Work with embeddings and knowledge-base integrations to improve LLM responses.

- Lead hands-on backend development using Python.

- Build scalable backend services, REST APIs, and microservices using FastAPI, Flask, or Django.

- Develop reusable, modular, and high-performance software components.

- Integrate AI models, databases, third-party services, and enterprise applications.

- Implement asynchronous processing, event-driven architecture, and distributed task execution.

- Write clean, maintainable, testable, and production-ready code.

- Design and deploy AI applications on cloud platforms such as AWS, Azure, or Google Cloud.

- Implement CI/CD pipelines, containerization, and orchestration using Docker and Kubernetes.

- Establish monitoring, logging, observability, and performance optimization practices.

- Build scalable model-serving and inference infrastructure.

- Ensure security, data privacy, availability, and fault tolerance across AI applications.

- Optimize infrastructure utilization and deployment costs.

- Provide architectural guidance and technical leadership to engineering teams.

- Mentor developers and establish coding standards and best practices.

- Collaborate with product managers, business stakeholders, and engineering teams to understand requirements.

- Conduct architecture reviews, code reviews, and technical assessments.

- Identify technical risks and recommend effective solutions.

- Stay updated with advancements in Agentic AI, Generative AI, LLMs, and software architecture.

Required Experience & Qualifications :

- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

- 10+ years of overall experience in software development, backend engineering, software architecture, or AI platform development.

- Strong hands-on experience in Python and backend application development.

- Proven experience designing and developing scalable microservices-based architectures.

- Practical experience building and deploying LLM-based applications and Agentic AI solutions.

- Strong understanding of RAG, vector databases, embeddings, and semantic search.

- Experience with cloud infrastructure, containerization, and production deployment.

- Ability to balance architectural decision-making with hands-on coding and implementation.

- Excellent problem-solving, communication, and technical leadership skills.

- Experience building enterprise-grade autonomous AI agents.

- Knowledge of AI observability, evaluation frameworks, and LLM monitoring.

- Experience with LangSmith, Langfuse, or similar tools.

- Understanding of model fine-tuning, quantization, and inference optimization.

- Knowledge of AI security, prompt injection prevention, and responsible AI practices.

- Experience with event-driven architectures, Kafka, Redis, or message queues.

- Exposure to MLOps and LLMOps practices

Skills

Agentic AI, Distributed Systems, Microservices Architecture, AI Integration, Python, REST API, FastAPI, Flask, Backend Architecture