Core GenAI Engineer

About Zycus

Zycus is a leading enterprise SaaS company revolutionizing procurement through AI-powered technology. Our Merlin AI Suite brings together Generative AI, Agentic AI, and intelligent automation to transform how enterprises manage procurement.

We are looking for exceptionally strong 2026 graduates who are passionate about building the next generation of AI-native enterprise products.

What You’ll Do

As a Core GenAI Engineer, you will work on building and deploying production-grade Generative AI capabilities that solve complex enterprise problems.

You will:

• Build and integrate LLM-powered applications, AI agents, and intelligent workflows.
• Develop RAG pipelines involving document processing, chunking, embeddings, retrieval, reranking, and context engineering.
• Work with LLMs, foundation models, prompt engineering, structured outputs, function/tool calling, and agentic workflows.
• Build data pipelines and preprocessing workflows required for training and inference.
• Experiment with different LLMs, embedding models, retrieval strategies, and AI architectures.
• Develop prototypes rapidly and take successful experiments towards production-ready systems.
• Implement evaluation frameworks to measure accuracy, relevance, hallucination, latency, and cost of GenAI applications.
• Work with frameworks such as LangChain, LangGraph, LlamaIndex or equivalent technologies.
• Integrate AI capabilities with APIs, databases, enterprise applications, and backend services.
• Collaborate with experienced engineers, product managers, and AI researchers to solve real-world enterprise problems.
• Continuously evaluate emerging GenAI technologies and identify opportunities to apply them to Zycus products.

What We’re Looking For

Education

• B.Tech/B.E./M.Tech/M.Sc. graduated in 2026.
• Preferred specializations:
  • Artificial Intelligence
  • Machine Learning
  • AI & ML
  • Data Science
  • Computer Science with AI/ML specialization
• Strong academic performance with good fundamentals in computer science and mathematics.

Technical Skills Must have:

• Strong programming skills in Python.
• Good understanding of Data Structures & Algorithms.
• Fundamentals of Machine Learning and Deep Learning.
• Hands-on exposure to Generative AI and LLMs.
• Understanding of NLP and Transformer architectures.
• Experience working with LLM APIs such as OpenAI, Anthropic, Gemini, or equivalent.
• Strong understanding of data handling using tools such as NumPy, Pandas, etc.
• Good understanding of Git/GitHub.

Good to have:

• Hands-on experience with RAG systems.
• Experience with LangChain, LangGraph, LlamaIndex or similar frameworks.
• Experience with vector databases such as FAISS, Chroma, Pinecone, Weaviate, Milvus, etc.
• Exposure to AI Agents, function calling, tool use, MCP, or agentic workflows.
• Exposure to model fine-tuning, PEFT/LoRA or open-source LLMs.
• Experience with PyTorch/TensorFlow.
• Knowledge of SQL and databases.
• Exposure to Docker, cloud platforms, APIs and microservices.
• Participation in AI/ML hackathons, Kaggle, research projects, open-source projects or technical competitions.

Internship / Project Experience

Prior hands-on experience through GenAI/AI/ML internships is highly preferred.

We especially value candidates who have built and deployed projects such as:

• RAG-based applications
• AI agents / multi-agent systems
• LLM-powered enterprise applications
• Document intelligence systems
• LLM evaluation frameworks
• AI-powered search/recommendation systems
• Fine-tuned language models
• Data/ML pipelines

GitHub or demonstrable project work will be a strong advantage.

What Will Make You Stand Out?

We are looking for candidates who build, experiment and learn by doing.

You will stand out if you can demonstrate:

• A strong GenAI project where you can explain the architecture and engineering decisions.
• Meaningful contributions during a GenAI/AI/ML internship.
• A strong GitHub portfolio.
• Participation in hackathons or AI competitions.
• Research papers, publications or open-source contributions.
• Ability to explain not only what you built, but why you built it that way and how you measured its performance.

Who Should Apply?

If you are a 2026 graduate who is excited about LLMs, GenAI and Agentic AI, enjoys solving complex technical problems, and wants to build AI systems that operate at enterprise scale, we'd love to hear from you.