AI Engineer
Skills
Artificial IntelligenceBusiness InnovationCode ReviewContextDiagram DesignEngineeringLarge Language Models
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Minimum of 3 years of professional experience in AI engineering or related roles
- 3+ years experience developing AI/ML solutions on platforms such as Snowflake Azure AWS OpenAI Databricks or similar
- 2+ years hands-on experience with Generative AI including LLM application development RAG systems and production deployments
- Experience with agentic AI frameworks such as LangGraph AutoGen CrewAI or OpenAI Assistants API and multi-agent orchestration
- Proficiency in Python LangChain or LlamaIndex and vector databases such as Pinecone Weaviate Chroma pgvector or Snowflake
- Expertise in prompt engineering including chain-of-thought few-shot learning and structured outputs
- Experience with evaluation frameworks for Generative AI such as RAGAs TruLens or DeepEval
- Understanding of AI safety concepts including guardrails content filtering hallucination mitigation and red-teaming
- Experience with data preprocessing feature engineering and model evaluation techniques
- Solid understanding of software engineering principles and best practices
- Experience bringing GenAI projects through production with measurable business impact
Nice to have
- Strong analytical skills
- Excellent communication
- Highly motivated
- Self-driven
- Creative thinker
- Effective time management
- Organizational skills
- Continuous learning mindset
Day to day
- Design and deploy production-grade AI solutions that automate workflows and improve customer experiences using LLMs and agentic frameworks.
- Build advanced RAG systems and generative AI applications with vector databases hybrid retrieval and reranking for domain-specific knowledge use cases.
- Implement AI observability guardrails evaluation and cloud-native deployment practices to ensure reliable safe and high-quality solutions.