Talent.com
Agentic Ai

Agentic Ai

ConfidentialNoida, India
6 days ago
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.

    • Skills Required

    Docker, Kubernetes

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