Applied AI Engineer

Position Overview: The Applied AI Engineer is responsible for building AI-driven applications by integrating Generative AI models, data pipelines, and inference systems into production-ready solutions This role involves hands-on development with Python and AI frameworks, designing RAG-based systems, optimizing model performance, and ensuring responsible AI deployment through guardrails and observability Key Responsibilities: 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