AI Engineer

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