As an AI Engineer, you will be responsible for rapidly prototyping, building, and deploying AI solutions leveraging LLMs, Generative AI, and AI agent frameworks. You will track the latest advancements in AI research and tools, turning concepts into working demos and POCs within days and thereafter productionize the AI solution. You’ll collaborate with both technical and business teams—supporting client engagements and business development through innovative AI demonstrations. 

Key Responsibilities 

• Design, develop, and prototype AI solutions using LLMs, Generative AI, and AI agentic frameworks. 

• Rapidly convert ideas into presentable demos and proof-of-concepts (POCs) in short timeframes. 

• Research, experiment, and benchmark emerging AI models, APIs, and agent frameworks. 

• Collaborate with business teams to create AI-powered solutions tailored for customer needs. 

• Drive AI projects end-to-end with minimal supervision, from ideation to deployment. 

• Support business development by showcasing innovative AI demos and prototypes. 

• Stay ahead of the curve on emerging protocols, frameworks, and research in the AI ecosystem.

Required skills and Experience 

• 2+ years of hands-on experience in Gen AI and Agentic AI engineering and building AI-powered products and solutions. 

• Proven experience working with vector DBs (qdrant, milvus, pinecone, etc).  

• Proficiency in Generative AI application development (langchain, Azure Foundry, Databricks Mosiac AI, etc) and optimally using LLM APIs with good know-how of different models (e.g., OpenAI, Anthropic, LLaMA, Mistral, Hugging Face etc.) and  

• Familiarity with MCPs and AI agent frameworks (LangGraph, AutoGPT, crewAI, N8n, A2A protocol). 

• Strong proficiency in Python or other programming languages for building ML/AI and software application. 

• Solid understanding of machine learning algorithms, deep learning architectures, and NLP techniques. 

• Ability to translate business use cases into technical AI solutions. 

• Experience deploying AI prototypes as demos, APIs, or microservices. 

• Experience deploying models in production using MLOps practices (CI/CD, MLflow, model versioning, A/B testing). 

• Good understanding of LLM and AI agents evaluation techniques and frameworks (Dspy, MLflow 3). 

• Strong problem-solving skills and ability to work independently in fast-paced environments. 

• Hands-on experience in building AI/ML workloads on one of public cloud platforms (Azure or AWS, or GCP). 

Good to Have 

• Experience building and fine-tuning Large Language Models (LLMs) and working with GenAI applications. 

• Understanding of reinforcement learning, computer vision, or time-series forecasting. 

• Familiarity with Infrastructure as Code (Terraform, Bicep) and containerization (Docker, Kubernetes). 

• Publications, patents, or active participation in AI communities and research forums.