Job Responsibilities

This is an excellent opportunity for someone early in their career to grow into advanced AI engineering, agentic architectures, and enterprise-scale AI delivery.

·Assist in designing, developing, and testing AI agents, GenAI workflows, and LLM-powered solutions.

·Support implementation of retrieval-augmented workflows (RAG) including embeddings, vector databases, and context engineering.

·Develop Python-based backend components, APIs, and integration logic for AI-driven systems.

·Collaborate with senior AI engineers to build multi-agent workflows, tool integrations, and agent orchestration pipelines.

·Contribute to development within AI platforms, Azure AI Foundry, and MCP-based tool ecosystems.

·Work with business stakeholders to understand problem statements and translate them into AI-enabled solutions.

·Perform testing, validation, and evaluation of LLM outputs, including applying guardrails and quality checks.

·Maintain documentation for prompts, workflows, tools, and AI system behaviors.

·Participate in Agile ceremonies and sprint activities within the AI engineering team.

Knowledge, Skills and Abilities

Education

·Bachelor’s degree in computer science, AI/ML/DS, Information Systems, Engineering, or a related field.

·Strong understanding of Python (preferred) or another major programming language (JavaScript/TypeScript, Java, etc.).

·Familiarity with foundational AI concepts:

·1–2 years of hands-on experience in AI/ML, Generative AI, or software engineering involving LLMs.

Knowledge and skills (general and technical)

·LLMs

·Embeddings

·Vector databases

·Prompt engineering

·Retrieval workflows (RAG)

·Experience with API development or integrating with REST-based services.

·Exposure to cloud platforms such as Azure (preferred), AWS, or GCP.

·Strong analytical and problem-solving skills.

·Good communication, a learning mindset, and the ability to collaborate with cross-functional teams.

Experience using Agentic AI frameworks such as:

·LangChain

·LangGraph

·Azure AI Agent Services

·Familiarity with MCP (Model Context Protocol) concepts and tool integrations.

·Basic understanding of vector search (Azure AI Search, Pinecone, FAISS).

·Experience with GitHub Copilot, OpenAI Vibe Coding, or similar AI coding assistants.

·Knowledge of cloud-native development (Functions, Storage, APIM, serverless patterns, Azure App Services).

·Understanding common AI safety and governance principles.

·Exposure to agile development practices.