GenAI Engineer
Confirmed on the employer's own hiring board on Oct 4, 2026. First seen by Alion on Oct 3, 2026.
We are looking for aGenerative AI Engineer with 3+ years of hands-on experience in building AI-driven applications. The ideal candidate will have strong expertise in machine learning, deep learning, and large language models (LLMs), with a passion for applying GenAI to solve real-world problems. You will collaborate with product, data science, and engineering teams to design, fine-tune, and deploy generative AI models at scale.
Key Responsibilities
Design, fine-tune, and deployLLMs and other generative AI models for production use cases.
Work with transformer architectures (GPT, BERT, LLaMA, etc.) for text, image, or multimodal tasks.
Buildend-to-end pipelines for training, inference, and evaluation of AI models.
Implement prompt engineering, RAG (Retrieval-Augmented Generation), and model optimization for improved performance.
Integrate GenAI capabilities intoweb, mobile, or enterprise applications.
Leverage frameworks such asLangChain, Hugging Face, TensorFlow, PyTorch.
Collaborate with backend/frontend teams to developAPIs and services that serve AI models.
Ensure AI solutions meetscalability, latency, and security requirements.
Research and stay updated on the latest advancements inGenerative AI, LLMOps, and ML infrastructure.
Requirements
Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.
4+ years of experience in machine learning, NLP, or AI development.
Proficiency inPython and libraries likePyTorch, TensorFlow, Hugging Face Transformers.
Solid understanding of LLMs, embeddings, vector databases (Pinecone, Qdrant, Weaviate, FAISS).
Experience withRAG pipelines, fine-tuning, or prompt engineering.
Familiarity with cloud platforms (AWS, GCP, Azure) for ML deployment.
Strong knowledge of APIs, microservices, and containerization (Docker, Kubernetes).
Experience withLangChain / LangGraph LlamaIndex for AI application orchestration.
Knowledge ofMLOps / LLMOps pipelines.
Exposure to multimodal AI (text, image, speech).
Hands-on experience withvector search optimizations.
Contributions to open-source AI projects.