Generative AI Engineer
Job Description
Job Title: Generative AI Engineer
For all roles India they want candidates who are ready to RTO. (Report to Office )
- 3 days a week with 4 hours overlap with US till 11pm IST
- On office reporting days it would mean they can leave office a bit early to make that 4 hour overlap with US
- On other 2 days remote work timing 1pm to 11pm IST
Employment Type: Full Time
Experience Level: Senior
About the Role
We are seeking a highly skilled and motivated Generative AI Engineer to design, develop, and deploy cutting-edge AI solutions leveraging Large Language Models (LLMs), multimodal transformers, and generative algorithms. You will be working on innovative applications across content creation, chat interfaces, autonomous agents, and intelligent data synthesis. This is a high-impact role where your work will help shape next-generation AI capabilities.
Key Responsibilities
- Develop and fine-tune LLMs (e.g., GPT, LLaMA, Mistral, Claude) for custom downstream tasks.
- Implement and optimize RAG (Retrieval-Augmented Generation) pipelines using tools like LangChain, LlamaIndex, or Haystack.
- Build end-to-end Generative AI applications for text, code, images, and audio.
- Leverage vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) for embedding-based retrieval.
- Integrate APIs from foundation models (OpenAI, Anthropic, Cohere, HuggingFace, etc.) into product workflows.
- Work with multi-modal models and techniques (e.g., CLIP, DALL·E, Stable Diffusion, Gemini, etc.).
- Optimize model performance, latency, and scalability in production environments.
- Collaborate cross-functionally with ML, data, and product teams to identify and implement use cases.
- Stay up-to-date with state-of-the-art advancements in generative AI and apply them proactively.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
- Experience in ML/AI engineering and strong experience with generative AI or LLMs.
- Proficiency in Python, with experience using libraries like Transformers (HuggingFace), LangChain, PyTorch, or TensorFlow.
- Strong understanding of NLP, deep learning, and transformer architectures.
- Experience building scalable ML pipelines and deploying models to production (Docker, Kubernetes, etc.).
- Familiarity with prompt engineering, fine-tuning, and model evaluation techniques.