If you are Interested, please fill this form: -

https://docs.google.com/forms/d/e/1FAIpQLSdrr1iAlbWgkrzfIiW4P13TGym9izzBFKZAa6QkgBbIycDi2w/viewform?usp=publish-editor

We are looking for a highly skilled 5 Years GenAI Engineer with robust hands-on experience in Generative AI, Agentic AI, LLM application development, data engineering, and LLMOps. The ideal candidate will be responsible for designing and developing production-grade GenAI and multi-agent solutions, implementing observability and governance, and integrating modern data and cloud platforms.

Key Responsibilities

- Design, develop, and deploy GenAI and Agentic AI applications using Python and modern LLM frameworks.
- Build multi-agent services and autonomous agent workflows with appropriate tool calling and orchestration.
- Develop and implement RAG architectures, embeddings, retrieval pipelines, and vector database solutions.
- Integrate LLM platforms such as OpenAI, Azure OpenAI, Anthropic, and AWS Bedrock.
- Implement agent and LLM observability, monitoring, and tracing using tools such as LangSmith, Langfuse, and Datadog.
- Monitor and optimize LLM/agent latency, token usage, cost, errors, tool calls, and model performance.
- Implement smart LLM routing and cost optimization across different models and cloud platforms.
- Design and implement multi-cloud GenAI solutions across AWS, GCP, Azure, or similar platforms.
- Develop Agentic AI governance and safety controls, including guardrails, human-in-the-loop workflows, audit logging, and responsible AI practices.
- Contribute to enterprise data strategy, data architecture roadmaps, and data-driven decision-making frameworks.




- Design and develop scalable data pipelines and data engineering solutions supporting GenAI applications.
- Work with modern data platforms such as Databricks, Snowflake, or similar lakehouse/data warehouse technologies.
- Implement CI/CD pipelines, automated testing, and release processes using Jenkins, GitHub Actions, Azure DevOps, or equivalent tools.
- Work with Infrastructure as Code tools such as Terraform.
- Apply MLOps/LLMOps practices for model deployment, monitoring, evaluation, and lifecycle management.
- Perform prompt engineering, LLM evaluation, fine-tuning/instruction tuning, and continuous improvement of GenAI solutions.

Required Technical Skills

Programming

- Strong hands-on experience with Python or similar programming languages.

Generative AI / LLM

- Hands-on experience developing production-grade GenAI applications.
- Experience with OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or equivalent LLM platforms.
- Strong understanding of prompt engineering, embeddings, LLM evaluation, and model behavior.

Agentic AI

- Hands-on experience building AI agents and multi-agent systems.
- Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.
- Understanding of agent orchestration, tool calling, autonomous workflows, and human-in-the-loop patterns.

RAG & Vector Databases

- Strong experience with RAG architectures.
- Experience with vector databases such as Pinecone, Weaviate, FAISS, pgvector, or similar technologies.
- Understanding of embedding generation, vector search, retrieval strategies, and document processing.

📌 Generative AI Engineer (Bengaluru)
🏢 Artech
📍 Bengaluru