Senior AI Engineer
AI Tools
Tech Stack
Agent Workflow
Build and manage agentic workflows that automate complex multi-step reasoning tasks. Develop advanced RAG pipelines.
About the Role
About Egen:
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights.
About the opportunity:
As a Senior AI Engineer, you will be at the forefront of our Generative AI initiatives. We treat AI as a software engineering discipline. You will be responsible for the full lifecycle of our AI features—specifically document intelligence and RAG pipelines—taking them from initial prototype to robust, scalable production services. You will solve for real-world constraints like latency, error handling, and cost optimization.
What You Will Do:
- Architect & Build: Design and implement end-to-end GenAI applications using Python, LangChain, and LlamaIndex on Google Cloud.
- Engineer for Precision: Develop advanced RAG (Retrieval-Augmented Generation) pipelines and Semantic Search systems using GCP Vector Search or Pinecone.
- Optimize Models: Lead efforts in LLM and Embedding fine-tuning to improve domain-specific performance.
- Agentic Ops: Build and manage agentic workflows that automate complex multi-step reasoning tasks.
- Collaborate & Innovate: Work directly with customers to understand requirements, suggest novel features, and implement state-of-the-art AI techniques.
- Productionize: Apply MLOps best practices to ensure models are served efficiently, monitored, and continuously improved.
Your Technical Toolkit:
- Core Languages: Mastery of Python and shell scripting.
- AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning.
- Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search.
- Infrastructure: Hands-on experience with GCP (Vertex AI) and building scalable software architectures.
- Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers.
Basic Qualifications:
- Proven track record of deploying GenAI products to a production environment.
- Experience with Classic Machine Learning is a strong plus.
- Knowledge of Data Engineering and SQL.