Senior AI Engineer

Qualifications and requirements

5+ years of professional software engineering experience, including hands-on AI/ML development.

Strong proficiency in Python, FastAPI, REST APIs, and backend architecture.

Practical experience developing applciations with LLMs and Generative AI.

Hands-on experience with RAG, LangChain, and vector databases.

Experience building AI agents using function calling, tool integration, and workflow orchestration.

Understanding of embeddings, prompt engineering, model evaluation, and inference optimization.

Experience with PostgreSQL, Redis, and vector search technologies such as pgvector, Pinecone, or FAISS.

Familiarity with PyTorch, Hugging Face, TensorFlow, or scikit-learn.

Experience deploying applications using cloud infrastructure and containerization.

Strong understanding of software farchitecture, performance optimization, testing, and production reliability.

Excellent problem-solving skills and ability to communicate technical decisions effectively.

Job functions

• Design, develop and deploy production-ready AI applications using Python, FastAPI and modern AI frameworks.
• Build LLM-powered applications using OpenAI, Claude, Gemini, and open-source models.
• Develop and optimize RAG pipe lines using Langchain, LangGraph, embeddings and vector databases.
• Architect AI agents, multi-agent systems and automated workflows with reliable tool integraions.
• Integrate AI Capability with backend APIs, PostgreSQL, Redis, and enterprise systems.Imple
• Implement model evaluation, prompt optimization, fine-tuning, and inference performance inprovements.

You will join an engineering team focused on developing intelligent applications that automate business processe, improve decision-making, and enhance operational efficiency.

The team works on LLM-powered applications, Retrieval-Augmented Generations (RAG), autonomous AI agents, and enterprise AI integrations.

you'll collaborate with software engineers, product managers, and business stakeholders to turn AI concepts into reliable, scalable products.

The focus is on building practical AI solutions that deliver measurable business value-not just prototypes or demonstrations.