About The Role
We are looking for a hands-on Gen AI Engineer to build real-world AI powered solutions on Google Cloud Platform. The role focuses on applying Generative AI to solve data-oriented problems, automate product workflows, and simplify complex business processes.
You will work on building intelligent systems using RAG (Retrieval Augmented Generation), AI agents, and data pipelines not just simple API integrations. The role requires both software engineering and practical AI problem-solving skills.
This is an applied engineering role where you will design and implement production-grade Gen AI applications rather than research-only experimentation.
Tech Environment
- Google Cloud Platform (GCP)
- Vertex AI & Gemini Models
- Python
- RAG Architectures
- Cloud Run deployments
- Data stores (SQL / NoSQL / Vector DBs)
- Gen AI Agents & workflow automation
Key Responsibilities
Gen AI Solution Development
- Design and implement Generative AI applications using Vertex AI and Gemini
- Build RAG pipelines integrating structured and unstructured data
- Develop AI agents for workflow automation and task orchestration
- Create intelligent assistants and automated decision systems
Data & Retrieval Systems
- Design data ingestion pipelines for AI consumption
- Work with embeddings, vector search, and knowledge retrieval
- Integrate databases, document stores, and business data sources into AI workflows
- Improve response quality using prompt design, retrieval logic, and evaluation
Product & Business Automation
- Automate product flows and operational processes using AI agents
- Build systems that assist internal teams in data analysis and decision-making
- Develop solutions to simplify business workflows and reduce manual operations
Cloud Deployment & Integration
- Deploy AI services on GCP (Cloud Run / APIs)
- Integrate AI features with backend services and applications
- Optimize cost, latency, and reliability of Gen AI workloads
Problem Solving
- Use Gen AI techniques to solve complex algorithmic and data processing problems
- Evaluate model outputs and continuously improve performance
- Create reusable AI frameworks and utilities
Requirements
Required Skills
Core Skills
- Strong Python programming (minimum 2 years)
- Hands-on experience with Generative AI tools (minimum 2 years)
- Good understanding of data models and data processing
- Practical implementation of RAG architectures
- Experience using Vertex AI or similar cloud AI platforms
- Working knowledge of Gemini / LLM APIs
- Understanding of AI agents and orchestration concepts
Cloud & Systems
- Experience with GCP services (preferred)
- Deploying services using Cloud Run or containerized APIs
- Working with databases and data stores (SQL/NoSQL)
- Handling large datasets and document collections
AI Engineering Knowledge
- Prompt engineering and evaluation techniques
- Embeddings and vector search concepts
- Handling hallucinations and improving answer accuracy
- API integration and microservices communication
What We Expect From You
- Ability to build complete GenAI solutions, not just call APIs
- Strong debugging and problem-solving mindset
- Interest in applying AI to real business problems
- Ownership mindset and experimentation attitude
- Ability to learn and adapt quickly in a fast-evolving AI ecosystem
Good to Have
- Experience with vector databases (Pinecone, Weaviate, FAISS, etc.)
- Experience with LangChain, LlamaIndex, or agent frameworks
- Knowledge of NLP fundamentals
- Experience building chatbots or AI assistants
- Exposure to data pipelines or ETL systems
- Experience optimizing LLM cost and latency
Experience
2 - 5 years total experience (At least 2 years hands-on with Python and 2 years working with Generative AI tools/platforms).
Benefits
Why Join Us
- Opportunity to work on real production Gen AI systems
- Build AI automation impacting business workflows
- Hands-on experience with Vertex AI & Gemini ecosystem
- Work on challenging data and algorithmic problems
Important Note
This role requires practical implementation experience. Candidates who have only completed courses or theoretical certifications in AI without building working applications may not be suitable.
