Interesting Job Opportunity: AI Engineer - LLM/RAG

Key Responsibilities

• Design, develop, and deploy AI-driven applications and intelligent software solutions.
• Build and optimize machine learning and deep learning models for production environments.
• Develop applications leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP).
• Fine-tune foundation models using domain-specific datasets and implement Retrieval-Augmented Generation (RAG) pipelines.
• Develop AI services and RESTful APIs for seamless integration with enterprise applications.
• Build scalable backend systems to support AI inference and model serving.
• Design data pipelines for model training, evaluation, and continuous improvement.
• Implement prompt engineering techniques to improve LLM performance and response quality.
• Optimize model accuracy, latency, scalability, and infrastructure utilization.
• Collaborate with software engineers, product managers, and data scientists throughout the product lifecycle.
• Deploy AI models using containerization and orchestration technologies.
• Implement MLOps practices for automated model training, deployment, monitoring, and version control.
• Evaluate emerging AI technologies and recommend suitable solutions for business use cases.
• Ensure AI applications meet security, governance, and responsible AI standards.
• Troubleshoot production issues and continuously improve system performance.

• Strong programming skills in Python.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Hands-on experience with Large Language Models (LLMs) and Generative AI technologies.
• Strong understanding of NLP, Transformers, Embeddings, and Vector Databases.
• Experience building RAG-based applications.
• Knowledge of prompt engineering and LLM optimization techniques.
• Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.
• Experience developing REST APIs using FastAPI or Flask.
• Strong understanding of data structures, algorithms, and software engineering principles.
• Experience with SQL and NoSQL databases.
• Familiarity with Docker, Kubernetes, and containerized deployments.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Knowledge of CI/CD pipelines and MLOps tools.
• Familiarity with Git and Agile development methodologies.

• Experience working with OpenAI, Anthropic, Google Gemini, or open-source LLMs such as Llama, Mistral, or Qwen.
• Exposure to AI agents, multi-agent systems, and autonomous workflows.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, or ChromaDB.
• Knowledge of distributed computing, GPU optimization, and model serving frameworks.
• Experience with monitoring and observability tools for AI applications.
• Understanding of responsible AI, model governance, and AI security best practices.

• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills.
• Ability to work independently and in cross-functional teams.
• Passion for innovation and continuous learning in AI technologies.
• Strong ownership, accountability, and attention to detail.
• Ability to thrive in a fast-paced product development environment.

(ref:hirist.tech)