AI & GenAI – Agentic AI & Cloud Engineer

Overview You will design, build, and deploy agentic AI solutions that combine GenAI (LLMs), RAG, and modern cloud/MLOps practices. You’ll work on end-to-end implementation—from prompt/agent design to scalable cloud deployment on AWS/Azure/GCP. Responsibilities - Build agentic AI workflows using LLMs, tool use, and multi-step task orchestration - Develop RAG systems with solid retrieval strategies (chunking, embeddings, re-ranking where applicable) for production-grade responses - Create and iterate on prompts, prompt templates, and evaluation approaches to improve reliability and quality - Implement RAG/agent pipelines using frameworks such as LangChain, LangGraph, and/or LlamaIndex - Integrate with LLM providers including OpenAI, Azure OpenAI, Gemini, AWS Bedrock, and Vertex AI - Manage vector database integrations and retrieval performance (e.g., indexing, querying, and lifecycle considerations) - Deploy and operationalize AI services on cloud platforms (AWS, Azure, and/or GCP) - Apply MLOps practices for CI/CD, monitoring, versioning, and safe rollout of AI features - Collaborate with product/engineering stakeholders to translate requirements into working AI solutions Requirements - 2–5 years of experience building production GenAI or agentic AI systems - Strong hands-on knowledge of LLMs and RAG concepts, including embeddings and vector-based retrieval - Experience implementing agentic workflows using LangChain and/or LangGraph and/or LlamaIndex - Proficiency in Python for building AI services and pipelines - Prompt engineering experience, including using prompts effectively with tool/agent behavior - Experience integrating LLMs from one or more of: OpenAI, Azure OpenAI, Gemini, AWS Bedrock, Vertex AI - Cloud experience with at least one major provider: AWS, Azure, or GCP - Working knowledge of MLOps practices (deployment pipelines, monitoring/observability, and model/solution versioning) Nice to have - Experience with Azure AI Foundry, Azure AI/ML services, or advanced managed GenAI tooling - Experience building with AWS Bedrock Agents or similar agent tooling - Familiarity with common vector databases and retrieval performance tuning - Experience with evaluation frameworks/approaches for LLM and RAG quality What we offer - Hybrid work mode in Bangalore, Chennai, Hyderabad, Pune, Mumbai, and Noida/Gurugram - Opportunity to work on practical agentic AI and cloud deployments with modern tooling - Collaborative environment focused on building production-ready GenAI systems Note: Tekwissen (tekwissen.com) will support your onboarding and project alignment based on skills and business needs.