Chat360 - Generative AI Engineer - Data Modeling

About Us :

Were an innovative team building next-generation AI products that transform how businesses operate and interact with users. From intelligent assistants to domain-specific LLM pipelines, were pushing the boundaries of generative AI to solve real-world problems.

Role Overview :

As a GenAI Engineer, youll be responsible for designing, implementing, and optimizing generative AI solutions using large language models (LLMs), retrieval-augmented generation (RAG), and related technologies. You will work closely with cross-functional teams including product, ML researchers, and platform engineers to deliver scalable and intelligent systems.

Key Responsibilities :

- Develop and deploy LLM-based applications using APIs or open-source models like OpenAI, Claude, LLaMA, Mistral, etc.

- Design and implement RAG pipelines combining LLMs with structured or unstructured data.

- Fine-tune and prompt engineer LLMs to align with product requirements.

- Optimize inference pipelines for latency and throughput.

- Collaborate with DevOps and platform teams to build robust and scalable infrastructure.

- Stay up to date with the latest trends and advancements in generative AI.

Requirements :

- Bachelors or Masters in Computer Science, AI/ML, or a related field.

- 1+ years of experience working with LLMs, transformers, or NLP-based systems.

- Proficiency in Python and Golang, and experience with frameworks like LangChain,

LlamaIndex, or Hugging Face Transformers.

- Solid understanding of vector databases (e.g., FAISS, Pinecone, Weaviate).

- Experience integrating APIs and managing cloud-based deployments (AWS, GCP, or Azure).

- Strong problem-solving skills and the ability to work in an agile environment.

Nice to Haves :

- Familiarity with Voice AI and speech interfaces (e.g., Whisper, ElevenLabs, Vosk, or real-time TTS/STT systems).

- Experience with real-time or low-latency systems.

- Understanding of multimodal models (text + image/audio).

- Prior experience with prompt optimization or LLMOps tooling.