About the Role :

We are expanding our AI team to build, scale and enhance our production-grade AI systems. As an AI Engineer, you will work across the full spectrum of AI - traditional ML, NLP, LLM-powered applications, conversational AI, multimodal AI (text, audio, video), agentic architectures, and evolving AI use cases - with a strong focus on Azure-based deployment.

Key Responsibilities :

- Design, build, and deploy AI/ML models and applications on Azure (Azure ML, Cognitive Services, AKS, Functions).

- Develop and integrate LLM-powered applications (Azure OpenAI, GPT-based APIs, Hugging Face models).

- Build and deploy chatbot and conversational AI systems for enterprise productivity, knowledge and information management.

- Develop traditional ML models (classification, prediction, recommendation) and combine them with LLM/NLP workflows.

- Architect and implement agentic AI systems and orchestration workflows.

- Work with vector databases (Pinecone, Weaviate, Milvus, FAISS, Qdrant) for retrieval-augmented generation (RAG).

- Ensure production readiness: CI/CD, monitoring, observability, performance tuning, and cost optimization.

Tech Stack & Requirements :

- 4 - 10 years in software/AI engineering with strong Python skills.

- Hands-on experience with traditional ML techniques, NLP pipelines, and using LLMs via APIs and SDKs.

- Familiarity with ML/AI libraries (PyTorch, TensorFlow, Hugging Face, LangChain/LlamaIndex/Semantic Kernel).

- Proven track record deploying AI applications on Azure.

- Experience with vector databases and embeddings for retrieval.

- Exposure to agentic systems and orchestrating multi-step reasoning.

- Skilled at building APIs/microservices and integrating AI into enterprise systems.

- Familiar with MLOps practices: CI/CD, model lifecycle, monitoring, and scaling.