Gen AI Engineer (Mid-Level)
Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Jul 10, 2026. FuGenTex Solutions scores D on the Alion truth index.
Position Overview
We are seeking a talented and passionate Gen AI Engineer with strong expertise in Large
Language Models (LLMs) to join our Backend Engineering team. In this role, you will design,
develop, and optimize AI-powered backend solutions that leverage the latest generative AI
technologies to build intelligent, scalable, and production-ready applications.
Key Responsibilities
Design, develop, and deploy backend applications powered by Large Language Models
(LLMs).
Build AI-driven services using modern Gen AI frameworks and APIs.
Develop and optimize prompt engineering strategies, Retrieval-Augmented Generation
(RAG) pipelines, and agent-based workflows.
Integrate LLMs with internal systems, APIs, databases, and backend services.
Evaluate, fine-tune, and optimize model performance, latency, accuracy, and cost.
Implement monitoring, testing, and guardrails to ensure reliability, security, and responsible
AI usage.
Collaborate with backend engineers, product managers, and stakeholders to translate
business requirements into AI-enabled solutions.
Stay up to date with emerging advancements in generative AI, LLMs, and AI infrastructure.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning,
or a related field.
Proven experience developing applications using Large Language Models (LLMs).
Strong programming skills in Python.
Experience with Gen AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, or
similar.
Hands-on experience with leading LLM providers such as Open AI, Anthropic, Google
Gemini, or open-source models (e.g., Llama, Mistral).
Experience designing and integrating RESTful APIs and backend services.
Strong understanding of vector databases, embeddings, Retrieval-Augmented Generation
(RAG), and prompt engineering.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
Knowledge of containerization and deployment technologies (Docker, Kubernetes) is an
advantage.
Excellent analytical, problem-solving, and communication skills.
Preferred Qualifications
Experience fine-tuning or adapting foundation models.
Experience with AI observability, evaluation frameworks, and model monitoring.
Familiarity with CI/CD pipelines and MLOps practices.
Experience building production-scale AI applications.
Understanding of AI governance, security, and responsible AI principles.
What You’ll Bring
Passion for generative AI and emerging technologies.