Agentic Ai

R Systems

6 years

0 Lacs

noida uttar pradesh india

Posted:1 week ago| Platform:

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Skills Required

ai engineering devops deployment monitoring scalability orchestration docker kubernetes azure aws ml stack design architecture programming layers retrieval summarization reasoning support strategies reliability data database management model training testing latency drift gitops openai security governance risk checks compliance audit collaboration

Work Mode

On-site

Job Type

Full Time

Job Description

Your Role and Impact

GenAI / Agentic AI Engineer (4–6 Years Experience)

Key Skills & Responsibilities

Strong expertise in Prompt Engineering for LLMs and SLMs.
Hands-on experience with SLMs and CrewAI for building and orchestrating multi-agent workflows.
Proficiency with Agentic AI frameworks (LangChain, LangGraph, etc.) and Generative AI solutions.
Experience in DevOps / LLMOps / MLOps, covering deployment, monitoring, observability, and CI/CD for AI systems.
Skilled in scalability and orchestration using Docker, Kubernetes, and Cloud / On-Prem environments.
Good understanding of the Azure , AWS AI/ML stack for enterprise-grade deployments.
Strong foundation in System Design & Architecture for distributed AI solutions.
Solid programming background with conceptual programming and best engineering practices.

Your Contribution

Roles & Responsibilities: GenAI / Agentic AI Engineer

GenAI / Agentic AI Development
Design, develop, and deploy agentic AI workflows using frameworks like CrewAI, LangChain, LangGraph, and custom orchestration layers.
Build and fine-tune SLMs and LLMs for task-specific use cases (retrieval, summarization, reasoning, decision support).
Implement prompt engineering & prompt chaining strategies for reliability and scalability.
System Architecture & Design
Contribute to AI solution architecture ensuring modular, scalable, and cloud-native designs.
Integrate AI components with enterprise systems, APIs, and external data sources.
Develop RAG pipelines, vector database integrations (Pinecone, Weaviate, FAISS), and multi-agent systems.
MLOps / LLMOps / DevOps
Own end-to-end lifecycle management: model training, testing, deployment, monitoring, and upgrades.
Implement observability & telemetry (latency, bias, drift detection, performance monitoring).
Ensure CI/CD pipelines for AI models and services with Docker, Kubernetes, and GitOps practices.
Cloud & Infrastructure
Deploy AI workloads on Azure AI/ML stack (Azure OpenAI, Cognitive Services, Azure ML).
Optimize AI solutions across cloud and on-prem environments for cost, performance, and security.
Ensure high availability and scalability of AI services.
Governance & Risk Management
Implement guardrails, safety checks, and compliance frameworks for responsible AI.
Conduct bias testing, red-teaming, and stress testing for models and agents.
Document AI system behavior, data lineage, and model decisions for audit readiness.
Collaboration & Knowledge Sharing
Work closely with data scientists, solution architects, and business stakeholders to translate requirements into agentic AI solutions.
Provide technical mentorship on SLMs, agent orchestration, and AI system design.
Support POCs, client demos, and technical workshops showcasing AI capabilities.

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