Applied AI Engineer

Job Description

We are seeking a talented Applied AI Engineer to design, develop, and deploy production-ready AI applications that solve real-world business challenges. In this role, you will work with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), machine learning models, and cloud technologies to build intelligent, scalable, and secure AI-powered solutions.

You will collaborate closely with data scientists, software engineers, product managers, and cloud architects to integrate AI capabilities into enterprise applications while ensuring high performance, reliability, and responsible AI practices.

Key Responsibilities

• Design, build, and deploy AI-powered applications using modern machine learning and Generative AI technologies.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for intelligent search and knowledge-based systems.
• Integrate Large Language Models (LLMs) into enterprise applications using APIs and AI orchestration frameworks.
• Build scalable AI microservices and REST APIs using Python frameworks such as FastAPI.
• Develop prompt engineering strategies to improve AI response quality and consistency.
• Evaluate AI model performance using quality metrics, benchmarking, and automated testing.
• Collaborate with software engineers to integrate AI services into web and cloud applications.
• Deploy AI applications on cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Optimize inference performance, latency, scalability, and cost efficiency for production AI workloads.
• Implement secure AI development practices, including data privacy, governance, and responsible AI standards.
• Monitor AI applications in production, troubleshoot issues, and continuously improve model performance.
• Stay current with advancements in AI, machine learning, and Generative AI technologies.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
• 3–7+ years of experience in software engineering, AI engineering, or machine learning development.
• Strong programming experience in Python.
• Experience developing production AI applications using Large Language Models (LLMs) and Generative AI.
• Hands-on experience with LangChain, LlamaIndex, or similar AI orchestration frameworks.
• Experience building REST APIs using FastAPI or Flask.
• Knowledge of machine learning fundamentals and model deployment.
• Experience with vector databases such as Pinecone, ChromaDB, Weaviate, or FAISS.
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Experience with Git, SQL, and Agile software development methodologies.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with OpenAI, Azure OpenAI Service, Anthropic Claude, Google Gemini, or other foundation models.
• Experience fine-tuning open-source LLMs using Hugging Face Transformers.
• Knowledge of MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Experience developing AI chatbots, virtual assistants, or intelligent automation solutions.
• Familiarity with AI governance, model evaluation, and responsible AI practices.
• Experience with cloud-native architectures and microservices.

Technical Skills

• Python
• FastAPI
• LangChain
• LlamaIndex
• OpenAI API
• Azure OpenAI
• Google Vertex AI
• Anthropic Claude APIs
• Hugging Face Transformers
• PyTorch
• TensorFlow
• Scikit-learn
• Pinecone
• ChromaDB
• FAISS
• PostgreSQL / SQL
• Docker
• Kubernetes
• Git
• GitHub Actions
• AWS / Microsoft Azure / Google Cloud
• REST APIs
• CI/CD

Benefits

• Competitive salary with annual performance bonuses.
• Medical, dental, and vision insurance.
• Flexible remote, hybrid, or on-site work options.
• Paid vacation, holidays, and wellness leave.
• Professional development budget and AI certification reimbursement.
• Access to the latest AI tools, cloud platforms, and development resources.
• Career advancement opportunities in AI engineering, machine learning, and technical leadership.
• Collaborative, innovative, and inclusive work environment.