AI Engineer

Role & responsibilities

1)Design, build, and deploy AI-powered applications and agentic workflows.
2)Develop solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, and open-source models.
3)Implement Retrieval-Augmented Generation (RAG) systems and knowledge retrieval frameworks.
Build and optimize AI agents, copilots, and workflow automation systems.
4)Develop APIs and backend services supporting AI products.
5)Create evaluation frameworks and benchmarks for AI model performance.
6)Fine-tune and optimize models where required.
7)Collaborate with product managers and business stakeholders to translate requirements into AI solutions.
8)Ensure scalability, security, reliability, and observability of deployed AI systems.
9)Stay current with advancements in Generative AI, multi-agent systems, knowledge graphs, and machine learning.

Preferred candidate profile

1)Strong proficiency in Python.
2)Experience with LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar frameworks.
3)Hands-on experience with OpenAI, Anthropic, Gemini, or open-source LLM ecosystems.
4)Experience with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, or Milvus.
5)Strong understanding of RAG architectures and prompt engineering.
6)Backend development experience using FastAPI, Flask, or similar frameworks.
7)Familiarity with cloud platforms such as Azure, AWS, or GCP.
8)Knowledge of Docker, containers, CI/CD, and software engineering best practices.
9)Experience with Git and collaborative development workflows.

Perks and benefits

Competitive and commensurate with experience, skills, and impact potential.