AI Engineer

Should have experience in the below: 

• Python (Advanced proficiency)
  


• PyTorch / TensorFlow (Model Development & Deployment)
  


• LangChain / LlamaIndex (LLM Orchestration)
  


• HuggingFace Transformers
  


• OpenAI, AWS Bedrock, Vertex AI, or Azure OpenAI APIs
  


• Vector Databases for RAG (Weaviate / Milvus / FAISS/mongodb)
  


• MongoDB / PostgreSQL (Structured Data Retrieval & Joins)
  


• Redis / DynamoDB (Fast Caching & Lookup)
  


• Kafka / RabbitMQ (Message Queues for Real-Time Inference)
  


• FastAPI / Flask (Backend APIs for ML Serving)
  


• Docker (Containerization for Model Inference)
  


• CI/CD Pipelines (GitHub Actions / GitLab CI / Jenkins)
  


• MLflow / Weights & Biases (Experiment Tracking & Model Management)
  


• S3 / GCS (Storage for Model Artifacts & Datasets)
  


• ElasticSearch / OpenSearch (Search over structured/unstructured text)
  


• Linting & Testing: Pytest, Black, Ruff, Flake8
  


• Type Hinting, Documentation Standards, YAML/JSON Config Management
  


• LLM training - finetuning (PEFT), pre-training
  


• Should be familiar with at least two Gen AI platform like Amazon Bedrock, Copilot, Vertex, etc..
  


• Should be familiar with at least two model like Claude, ChatGPT, Gemini, etc..
  


• Should be familiar with RAG Model and Agentic AI model
  

• Strong understanding of AI system design: data ingestion → preprocessing → model → retrieval → response
  


• Experience leading teams on LLM/NLP/ML projects end-to-end
  


• Excellent architectural decision-making & scalability mindset
  


• Familiarity with prompt engineering, evaluation metrics, and benchmarking
  


• Strong communication, documentation, and client-handling skills