Agentic Ai

Your Role and Impact

GenAI / Agentic AI Engineer (46 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.

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.