AI Engineer

Role overview This role focuses on building and shipping production-ready AI-powered applications, with a strong emphasis on large language models, generative AI, and human-centric system design. The work spans RAG architectures, prompt engineering, MLOps, and integration of AI capabilities into enterprise products. It suits an engineer with solid software fundamentals who enjoys turning research-grade AI into reliable, observable, and cost-effective production services.

Responsibilities - Build and deploy AI-powered applications using large language models and generative AI technologies. - Integrate AI solutions via APIs from providers such as OpenAI, Anthropic Claude, Azure OpenAI, and other platforms. - Implement Retrieval Augmented Generation (RAG) systems with vector databases and embedding models, and design prompt engineering frameworks with optimization pipelines. - Develop data pipelines for training, fine-tuning, and inference, and maintain MLOps infrastructure for deployment, monitoring, and versioning. - Optimize model performance, latency, and cost efficiency in production, and implement evaluation and monitoring systems for AI quality and behavior. - Collaborate with data scientists, architects, and product managers, contributing to code reviews and technical knowledge sharing.

Requirements - Strong proficiency in Python and experience with AI/ML libraries such as TensorFlow, PyTorch, or Hugging Face Transformers. - Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock) and prompt engineering techniques. - Experience building applications with modern frameworks (FastAPI, Flask, Django, or Node.js) and familiarity with vector databases like Pinecone, Weaviate, Chroma, or FAISS. - Experience with cloud platforms (AWS, Azure, or GCP) and their AI services, plus understanding of RESTful APIs, microservices, and containerization (Docker, Kubernetes). - Knowledge of Git-based version control, CI/CD practices, and data processing tools (Pandas, NumPy, SQL). - Understanding of model deployment and serving frameworks, monitoring and observability tools, and strong problem-solving skills.