Senior AI Engineer
First seen by Alion on Sep 16, 2026.
Key Responsibilities:
- Design and develop end-to-end AI/GenAI applications for enterprise use cases.
- Build and deploy solutions leveraging LLMs, RAG, prompt engineering, AI agents, and foundation models.
- Design AI application architecture covering data ingestion, model orchestration, APIs, business logic, and user interfaces.
- Develop scalable backend services and AI APIs using Python, REST APIs, microservices, and cloud-native technologies.
- Implement RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
- Work with vector databases and enterprise data sources to build reliable knowledge retrieval solutions.
- Integrate AI services with frontend applications and existing enterprise platforms.
- Develop AI solutions using cloud ecosystems such as Azure, AWS, or GCP.
- Build and maintain deployment pipelines for AI applications using CI/CD, containers, and DevOps/MLOps practices.
- Optimize AI applications for performance, scalability, reliability, security, and cost.
- Collaborate with data engineers, software engineers, product teams, architects, and business stakeholders.
- Contribute to technical solutioning, POCs, prototypes, and production implementations.
- Establish best practices for AI application development, testing, monitoring, and governance.
Required Skills & Experience :
- Strong hands-on experience in Python and AI application development.
- Strong understanding of LLMs, Generative AI, RAG, embeddings, prompt engineering, and foundation models.
- Experience with AI system design and solution architecture.
- Experience building enterprise-grade AI applications from concept to production.
- Strong experience with REST APIs, microservices, backend development, and API integration.
- Experience with vector databases such as Pinecone, Azure AI Search, FAISS, Weaviate, Milvus, or equivalent.
- Strong SQL and data-handling capabilities.
- Experience working with at least one major cloud platform: Azure, AWS, or GCP.
- Understanding of CI/CD, Docker, Kubernetes, DevOps/MLOps, and cloud deployment practices.
- Experience integrating AI/backend services with frontend applications.
- Understanding of AI application security, scalability, observability, and performance optimization.
Skills
Python, RAG, Prompt Engineering, Artificial Intelligence, Generative AI, Azure, SQL, Cloud, LLM