Responsibilities

- Design, build, and maintain production-grade LLM applications, AI agents, and agentic workflows.
- Implement agentic architectures using modern frameworks such as OpenAI Agents SDK, LangGraph, LangChain, CrewAI, LlamaIndex, or similar systems.
- Build RAG pipelines, context engineering workflows, structured tool use, and retrieval-based AI features.
- Integrate AI systems with APIs, databases, internal tools, external services, and MCP-compatible systems where relevant.
- Work with model providers such as OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, or similar platforms.
- Design multi-agent workflows with clear task orchestration, tool routing, memory/context handling, and failure recovery.
- Build response evaluation, automated testing, and quality monitoring workflows for AI outputs.
- Implement guardrails for prompt injection, unsafe tool use, data leakage, PII handling, and hallucination risk.
- Optimize LLM usage for latency, accuracy, token consumption, and cost efficiency.
- Work with vector databases and retrieval systems such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or similar tools.
- Collaborate with product and engineering teams to turn AI use cases into reliable user-facing systems.
- Use AI coding tools such as Cursor, Claude, and Codex as part of day-to-day engineering workflows.

Requirements

- AI Systems and Agentic Architecture 3+ years of hands-on software engineering or AI systems engineering experience.
- Practical experience building LLM-powered applications or AI agents for production use.
- Strong understanding of agentic architecture, tool use, RAG, orchestration, and context engineering.
- Hands-on experience with frameworks such as OpenAI Agents SDK,



LangGraph, LangChain, CrewAI, LlamaIndex, or similar tools.
- LLM Engineering and Evaluation Strong understanding of prompt engineering, structured outputs, context windows, token usage, and model tradeoffs.
- Experience building evaluation datasets, automated evals, regression checks, and response-quality monitoring workflows.
- Understanding of hallucination risk, prompt injection, guardrails, AI safety, and production reliability patterns.
- Ability to optimize AI workflows for accuracy, latency, cost, and maintainability.
- Development and Infrastructure Robust Python development skills and experience designing APIs.
- Experience integrating model APIs and external systems into production applications.
- Basic knowledge of AWS services, CI/CD workflows, and Docker-based deployment environments.
- Working knowledge of SQL and NoSQL databases, including PostgreSQL, MongoDB, and Redis.
- Experience with vector databases or retrieval infrastructure.
- Experience with AI coding tools such as Cursor, Claude, and Codex.

Preferred Skills

- React knowledge for collaborating on AI-powered user interfaces.
- Experience with Vercel AI SDK, streaming AI interfaces, or conversational UI patterns.
- Experience building voice agents or multimodal AI workflows.
- Experience with n8n or business process automation workflows.
- Exposure to Kubernetes or container orchestration platforms.
- Understanding of transformers, tokenization, embeddings, and inference behavior.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Software Engineer - Python (Ahmedabad)
🏢 Bitontree
📍 Ahmedabad