Agentic / Retrieval-Augmented Generation (RAG) AI Engineer

About the job

About the Role We are seeking a passionate Agentic / Retrieval-Augmented Generation (RAG) AI Engineer to design, build, and optimize intelligent automation systems that integrate large language models (LLMs) with software development life cycle (SDLC) workflows used in avionics and safety-critical software projects. You will develop autonomous AI agents, RAG pipelines, and reasoning systems that streamline documentation, verification, and compliance processes in line with aerospace standards. Key Responsibilities • Design and implement RAG pipelines integrating LLMs with vector databases and retrieval systems to support traceability, requirements validation, and code-document alignment. • Develop autonomous AI agents capable of multi-step reasoning, task execution, and contextual decision-making across software lifecycle phases (requirements, design, code, test, verification). • Fine-tune and evaluate LLMs and embeddings for domain-specific avionics use cases and engineering documentation. • Integrate APIs and frameworks such as OpenAI, Anthropic, LangChain, and LlamaIndex into AI-driven SDLC automation workflows. • Work with vector databases (e.g., Pinecone, Chroma, Weaviate, Milvus) for efficient retrieval and knowledge management. • Implement context and memory management systems enabling agents to maintain state and learn from interactions. • Collaborate with avionics domain experts and cross-functional teams (ML, backend, and verification engineers) to deploy scalable and compliant AI solutions. • Monitor, evaluate, and optimize agent performance for accuracy, latency, cost, and reliability. • Stay current with advancements in LLMs, RAG architectures, and AI agents, applying best practices to regulated software domains. Desirable Skills & Domain Knowledge • Familiarity with avionics software development processes, including DO-178C, ARP4754A, and MISRA C guidelines. • Understanding of model-based design, requirements traceability, and automated verification workflows. • Knowledge of configuration management, version control, and continuous integration in safety-critical systems. • Awareness of software assurance, review automation, and document generation practices for aerospace projects. • Strong analytical and problem-solving skills with the ability to bridge AI technologies and engineering domain expertise. Preferred Technical Stack • Languages : Python, C / C++ is a plus • Frameworks : LangChain, LlamaIndex, OpenAI API, FastAPI • Databases : Chroma, Weaviate, Pinecone, Milvus • ML Tools : Hugging Face, TensorFlow, PyTorch • Deployment : Docker, Kubernetes, Azure / OpenShift, CI / CD pipelines Qualifications • Bachelor’s or Master’s in Computer Science, Artificial Intelligence, or related field (Aerospace / Embedded domain exposure preferred). • 1–2 years of experience in AI / ML engineering, with hands-on work in LLM or RAG systems. • Exposure to aerospace or defence software development is an added advantage.

Requirements

• AI Engineering
• RAG Systems
• LLMs
• Software Development Life Cycle

Qualifications

• Bachelor’s or Master’s in Computer Science, AI, or related field

Preferred Technologies

• AI Engineering
• RAG Systems
• LLMs
• Software Development Life Cycle