Job Description
As an Agentic AI Engineer, you will be at the forefront of AI innovation, working with a diverse team to create intelligent, autonomous systems. Your role is pivotal in enhancing our products and services, ensuring they are at the cutting edge of technology. You will collaborate with cross-functional teams, applying your expertise to develop and implement AI strategies that drive our business forward.
How You Will Contribute And What You Will Learn
- Design, develop, and optimize agentic AI solutions for a variety of business and customer use cases.
- Transform existing processes and automation tools into intelligent, autonomous, and semi-autonomous workflows.
- Define and implement solution architectures for agent orchestration, memory management, tool integration, guardrails, and human-in-the-loop workflows.
- Enhance process efficiency, user experience, and operational scalability through AI-driven automation solutions.
- Document solution designs, technical decisions, best practices, and implementation guidelines to support scalable delivery and maintenance.
Key Skills And Experience
Must-Have:
- Bachelor's degree with experience in AI/ML engineering and solution development.
- Strong experience in AI/ML engineering, solution architecture, and end-to-end AI solution delivery.
- Deep understanding of RAG, prompt engineering, vector databases, embeddings, and model evaluation techniques.
- Hands-on experience with LLMs, generative AI technologies, and agentic AI frameworks for building intelligent applications and orchestration workflows.
- Strong knowledge of software engineering best practices, including APIs, automation platforms, version control, testing, CI/CD, deployment, and responsible AI principles related to governance, security, and privacy.
Nice-to-Have:
- Experience with LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK, or equivalent agent orchestration frameworks.
- orchestration frameworks for intelligent automation.
- Automated infrastructure provisioning and management using Terraform and Infrastructure as Code (IaC) practices.
- Designed and deployed cloud-based solutions on Azure, AWS, and Google Cloud platforms.
- Applied MLOps best practices for enterprise AI deployments and leveraged automation certifications to deliver scalable, reliable solutions.
