AI Engineer
2 Required Technical Skill Set** AIML, NLP, Langchain, RAG, Llama, Azure Open AI
4 Desired Experience Range** 4+ Years
5 Location of Requirement Bangalore
Desired Competencies (Technical/Behavioral Competency)
Must-Have** · The AI Engineer will be responsible for designing and developing innovative Proof of Concepts (POCs) using advanced AI/ML and Generative AI technologies across various business applications. The role involves working closely with business stakeholders and cross-functional teams to translate problem statements into scalable AI-driven solutions. The candidate is expected to have strong hands-on experience in machine learning, LLMs, and rapid prototyping, along with the ability to evaluate feasibility and deliver working prototypes. · • Strong hands-on experience in Machine Learning, NLP, and Generative AI • Experience working with LLMs (GPT, Llama, Mistral, Claude, etc.) • Proficiency in frameworks such as LangChain, LlamaIndex, or Semantic Kernel • Experience in building AI/ML prototypes and rapid experimentation • Strong programming skills in Python and API development • Understanding of embeddings, vector databases, and RAG architectures • Knowledge of cloud platforms (Azure/AWS/GCP), preferably Azure OpenAI • Strong analytical, problem-solving, and communication skills • Ability to work in Agile and collaborative environments
Key responsibilities: • • Design and develop AI/ML Proof of Concepts (POCs) to validate new business use cases • Build and deploy LLM-based applications using modern frameworks and custom pipelines • Rapidly evaluate feasibility across ML, NLP, and Generative AI domains • Develop APIs, microservices, and pipelines to integrate AI solutions • Conduct experiments on model fine-tuning, embeddings, and vector search optimization • Stay updated with the latest advancements in AI (transformers, RAG, agentic workflows, multimodal models) • Collaborate with business stakeholders to convert requirements into technical solutions • Present POC outcomes, insights, and recommendations to leadership • Apply lightweight MLOps practices such as versioning, monitoring, and evaluation
