Role Description
This role focuses on building, fine-tuning, and deploying generative AI systems such as large language models (LLMs), copilots, chatbots, and AI agents. It combines machine learning engineering, software development, prompt engineering, and AI system design.
- Generative AI Engineer focuses on building GenAI applications, prompt systems, and LLM-powered features
- LLM Engineer focuses on working directly with large language models, fine-tuning, retrieval systems, and inference optimization
- GenAI Lead focuses on leading GenAI architecture, strategy, team leadership, and enterprise-scale AI product delivery
Key Responsibilities
- Designing and building LLM-powered applications (chatbots, copilots, AI agents)
- Developing prompt engineering systems and retrieval-augmented generation (RAG) pipelines
- Fine-tuning and optimizing large language models for specific business use cases
- Building AI APIs and integrating LLMs into production systems
- Improving model performance, latency, accuracy, and cost efficiency
- Working with data pipelines, embeddings, vector databases, and knowledge systems
- Deploying and scaling GenAI systems in cloud environments
- Collaborating with Product, Data Science, and Engineering teams
- Ensuring responsible AI usage, safety, and compliance
- Researching and applying latest advancements in LLMs and generative AI
Qualifications (Must-have)
- Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or related field
- 3–10+ years of experience in software engineering, ML engineering, or AI development
- Strong programming skills in Python (primary), plus JavaScript or Java
- Solid understanding of machine learning, deep learning, and NLP concepts
- Experience building production-grade software systems
- Strong problem-solving and system design skills
- Familiarity with APIs, cloud services, and scalable architectures
- Ability to work with fast-evolving AI technologies
Preferred Qualifications
- Experience with LLM and GenAI platforms such as OpenAI, Anthropic Claude, or Google Gemini APIs
- Familiarity with ML frameworks such as PyTorch or TensorFlow
- Experience with vector databases like Pinecone, Weaviate, or FAISS
- Knowledge of RAG architectures, embeddings, and semantic search systems
- Experience with cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud
- Familiarity with MLOps tools such as Databricks, MLflow, or Kubeflow
- Experience building AI agents, copilots, or enterprise automation systems
- Strong understanding of prompt engineering, tool calling, and function calling
- Experience in SaaS, fintech, healthcare, or enterprise AI applications
- Prior experience as ML Engineer, AI Engineer, or Backend Engineer is common
- PhD or research background in AI/NLP is a plus for GenAI Lead roles