Associate Software Architect - Agentic AI Solutions
Designs and hands-on builds production-ready agentic AI systems: LLM-powered autonomous agents, multi-agent orchestration, and RAG pipelines, plus the Python backend services (FastAPI/Flask/Django), microservices, and cloud deployment (AWS/Azure/GCP with Docker/Kubernetes) behind them. A senior architecture-plus-coding role for engineers with 10+ years' experience.
- Compensation
- Not disclosed
- Location
- Bhubaneshwar; Chandigarh; Odisha
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Architecture
- Posted
Skills
Must have
- Bachelor's or Master's degree in Computer Science, IT, Engineering, or related field
- 10+ years 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
Full description
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.