AI Engineer
Tasks
- Apply traditional ML and statistical modeling
- Architect multi-agent AI systems
- Build production RAG and Graph RAG systems
- Develop Python backend services for LLM inference orchestration
- Establish LLMOps and MLOps pipelines
- Implement agent state and context management
- Implement responsible AI and AI governance
- Lead LLM fine tuning and model alignment
- Lead development of generative AI pipelines
- Research evaluate and productionize generative AI advancements
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Neptune | Agent systems | Agentic AI | Anthropic | Async Programming | Autogen | Bias detection | CrewAI | Databricks | Deep learning | Explainability | FastAPI | Feature Store | GPT-4o | Gemini | Generative AI | Graph RAG | Hallucination mitigation | Hugging Face | Instruction Tuning | LLMOps | Langchain | Langgraph | Language Models | Large Language Models | LoRA | MLOps | MLflow | Machine Learning | Multi-Agent | Multi-Agent Systems | Neo4j | OpenAI | OpenAI GPT | OpenAI GPT-4o | OpenSearch | PEFT | PGVector | Pinecone | Prompt engineering | PyTorch | Python | RLHF | Responsible AI | Retrieval-Augmented Generation | SQL | Semantic Search | TensorFlow | Tool use | Vector embeddings
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Lead | Lead Engineer
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