Agentic AI Engineer - LLM/RAG
- Compensation
- Not disclosed
- Location
- Hyderabad; Bangalore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- - 3+ years of experience in AI engineering, applied ML, or data science, with demonstrated hands-on delivery.
- - Strong Python proficiency and experience working with ML / AI frameworks.
- - Hands-on experience with LLMs and chatbot or conversational AI systems, including workflow design.
- - Practical experience with agentic AI concepts, such as tool invocation, decision routing, and orchestration logic.
- - Experience designing or implementing RAG architectures (vector search, embeddings, retrieval pipelines).
- - Working familiarity with at least one Cloud Service Provider (Azure, AWS, or GCP), including deploying or integrating AI services in cloud environments.
- - Ability to think beyond "model training and focus on where outputs live, how they are consumed, and how they drive action.
- - Strong communication skills and comfort collaborating across engineering, platform, and business teams.
- Preferred / Nice-to-Have Skills :
- - Experience standing up or working with MCP servers, tool servers, or agent runtime environments.
- - Exposure to FinOps, cloud cost optimization, or infrastructure automation use cases.
- - Familiarity with Azure OpenAI, AWS Bedrock, or GCP Vertex AI.
- - Experience deploying AI components as APIs or microservices.
- - Exposure to MLOps or LLMOps practices (monitoring, versioning, evaluation, prompt management).
- - Understanding of enterprise governance, security, and compliance considerations for AI systems.
- Why Work With Us :
- - Work on real-world agentic AI systems, not experimental or isolated models.
- - Join a high-impact AI enablement team supporting enterprise-scale FinOps and cloud transformation.
- - Gain hands-on exposure to chatbots, agentic workflows, and production LLM systems in a CSP environment.
- - Collaborate with experienced cloud, automation, and AI architects on emerging enterprise use cases.
- - Opportunity to grow into advanced AI engineering and agent orchestration roles
Full description
Role Overview :
We are seeking an AI Engineer to join an AI enablement team supporting a large-scale FinOps and cloud cost optimization program for a Fortune 50 enterprise. This role sits at the intersection of agentic AI, chatbot development, and applied data science, with a strong emphasis on designing end-to-end workflows that allow AI models to interact with users, systems, and automations in production environments.
You will help design and implement chatbots and agent-driven systems that leverage LLMs, RAG pipelines, and decision logic, ensuring that model outputs are not isolated artifacts but are properly integrated into chat interfaces, orchestration layers, and downstream automations. While this role requires solid data science and ML foundations, it is primarily focused on AI engineering and real-world deployment, not academic modeling.
This is a hands-on role within a growing agentic AI delivery team, working closely with cloud, FinOps, and automation engineers.
Key Responsibilities :
- Design and build chatbot workflows, including how models, tools, and data sources interact within conversational and agent-driven systems.
- Develop agentic AI workflows using frameworks such as LangChain, LangGraph, or equivalent orchestration patterns.
- Implement RAG pipelines that combine LLMs with structured and unstructured data sources for reasoning and decision-making.
- Build and integrate LLM-powered services where model outputs are surfaced via chat interfaces, APIs, or automation triggers.
- Design decision-making flows where agents invoke tools, APIs, databases, or cloud services as part of execution.
- Collaborate with cloud and platform teams to ensure AI components are deployable, scalable, and secure within CSP environments.
- Support evaluation, iteration, and refinement of AI behaviors, prompts, tools, and workflows over time.
- Document architectures, workflows, and assumptions to support maintainability and enterprise adoption.
Required Skills & Experience :
- 3+ years of experience in AI engineering, applied ML, or data science, with demonstrated hands-on delivery.
- Strong Python proficiency and experience working with ML / AI frameworks.
- Hands-on experience with LLMs and chatbot or conversational AI systems, including workflow design.
- Practical experience with agentic AI concepts, such as tool invocation, decision routing, and orchestration logic.
- Experience designing or implementing RAG architectures (vector search, embeddings, retrieval pipelines).
- Working familiarity with at least one Cloud Service Provider (Azure, AWS, or GCP), including deploying or integrating AI services in cloud environments.