Generative AI Engineer
First seen by Alion on Aug 11, 2026.
Key Responsibilities :
- Design, develop, and deploy Generative AI and LLM-based applications.
- Build and optimize RAG pipelines for enterprise AI use cases.
- Work with LLMs, prompt engineering, embeddings, and vector search technologies.
- Develop AI/ML solutions using Python and relevant frameworks.
- Design knowledge graphs and graph-based solutions using Neo4j.
- Integrate LLMs with enterprise applications, APIs, databases, and external services.
- Evaluate model performance, response quality, accuracy, and relevance.
- Implement techniques to improve LLM responses, including prompt optimization and retrieval strategies.
- Collaborate with product, engineering, and business teams to translate requirements into scalable AI solutions.
- Follow best practices for AI security, scalability, monitoring, and responsible AI development.
Required Skills & Experience :
- Strong hands-on experience in Generative AI / AI/ML.
- Experience working with Large Language Models (LLMs).
- Strong understanding of RAG architecture and implementation.
- Proficiency in Python and AI/ML frameworks.
- Hands-on experience with Neo4j or other graph databases.
- Experience with embeddings, vector databases, semantic search, and knowledge retrieval.
- Understanding of prompt engineering and LLM evaluation techniques.
- Experience integrating AI solutions through REST APIs and cloud platforms.
- Strong problem-solving and analytical skills.
Good to Have :
- Experience with LangChain, LlamaIndex, or similar GenAI frameworks.
- Exposure to OpenAI, Azure OpenAI, Gemini, Claude, or other foundation models.
- Experience with vector databases such as Pinecone, FAISS, Weaviate, or Milvus.
- Knowledge of cloud platforms such as AWS, Azure, or GCP.
- Experience with AI agents and agentic workflows.
- Exposure to MLOps, CI/CD, Docker, and Kubernetes.
Skills
Machine Learning, Python, Artificial Intelligence, Generative AI, LLM, Data Science, RAG, Knowledge Graph, LangChain