Applied AI Engineer

• Design, implement, and optimize Generative AI applications using Python and frameworks such as FastAPI. 

• Build AI solutions using LLM frameworks like LlamaIndex and LangChain. 

• Implement containerized deployments using Docker. 

• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for improved information retrieval. 

• Work with self-hosted and cloud-based vector databases for efficient search and retrieval. 

• Design and manage knowledge graphs and graph-based RAG systems. 

• Implement re-ranking models and retrieval optimization techniques. 

• Apply prompt engineering and context engineering to enhance model performance. 

• Establish guardrails to ensure safe, ethical, and compliant AI deployments. 

• Build data preprocessing and transformation pipelines for structured and unstructured data. 

• Perform inference using offline LLMs via platforms like Ollama or Hugging Face (Llama, Mistral). 

• Integrate online LLM providers such as OpenAI, Anthropic, or GCP for real-time inference. 

• Monitor AI workflows using observability tools like MLflow or Arize Phoenix. 

• Evaluate model performance using frameworks such as TruLens or custom-built evaluation systems. 

• Continuously improve AI systems based on evaluation insights, metrics, and user feedback. 

Skills for a Generative AI Engineer:

• Experience building Generative AI applications using Python and FastAPI. 

• Hands-on knowledge of LLM frameworks such as LangChain or LlamaIndex. 

• Ability to work with unstructured data (PDFs, documents, chunking, search) and structured data. 

• Experience designing RAG-based systems, including prompt engineering and retrieval optimization. 

• Familiarity with vector databases (Qdrant, Pinecone, Weaviate) and search solutions. 

• Exposure to AI agents, workflows, and basic orchestration concepts. 

• Experience using cloud platforms like Azure or AWS. 

• Working knowledge of online and offline LLMs (OpenAI, Llama, Mistral). 

• Understanding of AI evaluation, monitoring, and observability concepts. 

• Experience with Docker and CI/CD pipelines for deploying AI applications. 

Good to Have: 

• Experience with MCP clients and servers 

• Knowledge of multimodal LLMs for image and voice processing 

• Knowledge of deploying applications in cloud or on-prem infrastructure 

• Knowledge of fine-tuning techniques and data preparation for fine-tuning 

Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field 

• Proven experience in AI/ML engineering and related technologies 

• 3+ years of experience building applications using Python and asynchronous programming 

• Experience working with SQL and NoSQL databases 

• Strong problem-solving skills and ability to work in a fast-paced environment 

  • Excellent communication and teamwork skills