Junior AI Engineer

Job description

Educational Requirements - Bachelor of Engineering Responsibilities - GenAI / LLM Engineering: Build LLM-powered applications (chatbots, copilots, summarization, knowledge assistants) using OpenAI/Azure OpenAI/Anthropic/Gemini or open-source LLMs. - Implement RAG pipelines: data ingestion, chunking, embeddings, vector search, prompt assembly, response generation. - Improve response quality using prompt engineering, retrieval tuning (hybrid search, metadata filters), and basic RAG evaluation practices. - ML Engineering (non-platform): Develop and deploy ML components (classification, NLP, forecasting) using scikit-learn / PyTorch / TensorFlow as needed. - Package AI/LLM solutions into production-grade services using FastAPI/Flask. - Write clean, reusable Python modules and follow engineering best practices (testing, logging, code quality). - Deployment Operations (LLMOps exposure): Support deployment to cloud environments: AWS (SageMaker/ECS/Lambda) or Azure (Azure ML/AKS/App Services). - Implement basic observability: logs, error handling, latency tracking, token usage tracking (where applicable). - Assist in quality, safety, and governance practices: PII redaction, content filtering, prompt-injection mitigation, secure access controls. Additional Responsibilities - Vector databases: Pinecone / Qdrant / Chroma / Weaviate / FAISS. - Frameworks: LangChain / LangGraph / LlamaIndex / Semantic Kernel. - Evaluation tools: RAGAS / TruLens / DeepEval, prompt testing frameworks. - Containerization: Docker (Kubernetes is optional). - CI/CD exposure: GitHub Actions / Azure DevOps / Jenkins. - Data pipelines: Airflow / Prefect / Databricks. - Safety tooling: Presidio, content safety filters, access control patterns. Technical and Professional Requirements - Python programming (strong fundamentals, OOP, writing APIs, debugging). - Hands-on experience building GenAI/LLM solutions: RAG / embeddings / vector DB / prompt engineering. - Experience with FastAPI or Flask (building and serving APIs). - Understanding of LLM application lifecycle (prompting, evaluation, versioning, deployment basics). - Knowledge of at least one cloud platform: AWS or Azure. - Basic understanding of Git, code reviews, and deployment workflows. Preferred Skills - Technology- AI-Generative AI- Artificial Intelligence - BASIC - Technology- AI-Generative AI- Generative AI - Basic