AES-AI Engineer

Job Description -AI Engineer

Experience: 2–7 Years

About the Role

We are looking for a Generative OR Agentic AI Engineer with hands-on experience in building AI-powered applications using LLMs, Agentic AI frameworks, and modern cloud platforms. The ideal candidate should be able to independently prototype, develop, and demonstrate innovative AI solutions while effectively communicating with both technical and business stakeholders.

Key Responsibilities

Generative AI & Agentic AI

• Build end-to-end GenAI applications using OpenAI, Claude, Gemini, Mistral, Llama, etc.
• Develop AI agents and multi-agent systems using LangGraph, AutoGen, CrewAI, LlamaIndex, or similar frameworks.
• Design and implement RAG-based applications with vector databases and embedding models.
• Rapidly create and showcase AI PoCs and production-ready solutions.

AI Innovation & Experimentation

• Fine-tune LLMs/SLMs for domain-specific use cases.
• Create synthetic datasets for training and evaluation.
• Evaluate AI agents for performance, reliability, accuracy, and latency.
• Explore emerging standards such as MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication.

Collaboration & Communication

• Translate business requirements into AI-powered solutions.
• Present AI concepts, demos, and outcomes to technical and non-technical audiences.
• Collaborate with cross-functional teams to drive AI innovation initiatives.

Required Skills

• Strong proficiency in Python, AI/ML, MLOps, Prompt Engineering, Embeddings, and Model Orchestration
• Hands-on experience with LLMs, Agentic AI, RAG, Vector Databases, and Cloud Platforms (AWS/Azure/GCP/Databricks)
• Experience building scalable AI applications beyond notebooks.
• Strong problem-solving, presentation, and stakeholder management skills.

Good to Have

• Microsoft Copilot Studio
• Databricks Mosaic AI
• DevRev
• Cursor
• Codium
• Atlassian AI

Qualifications

• Bachelor's/Master's degree in Computer Science, AI/ML, or related field.
• 2–7 years of experience in Software Development, AI/ML, or GenAI Engineering.
• Proven ability to independently drive AI experiments from ideation to working demos and production-ready solutions.