Generative AI Engineer

Key Responsibilities :

- Design, implement, and optimize Generative AI models (LLMs, VAEs, GANs, Diffusion Models, Transformers, etc.) for text, image, and multimodal applications.

- Fine-tune and deploy foundation models (such as GPT, Llama, Mistral, Claude, or Stable Diffusion) for domain-specific use cases.

- Build data pipelines for curating, preprocessing, and augmenting training datasets to improve model performance and reliability.

- Collaborate with data engineers and ML ops teams to deploy, monitor, and scale AI models in production environments.

- Conduct rigorous model evaluation, including quantitative performance metrics, bias detection, and explainability assessments.

- Stay current with the latest AI/ML research, frameworks, and tools, and propose innovative ideas for integrating them into business solutions.

- Work closely with cross-functional stakeholders (product, design, and engineering) to translate business challenges into AI-driven solutions.

- Contribute to building internal AI accelerators, frameworks, and reusable components to streamline generative model development.

- Ensure ethical AI practices, data governance, and model transparency in all implementations.

Core Technologies & Tools :

- Languages : Python (preferred), PyTorch, TensorFlow, JAX

- Generative AI Frameworks : Hugging Face Transformers, LangChain, OpenAI APIs, Stability SDK, Diffusers

- ML Tools & Libraries : Scikit-learn, NumPy, Pandas, ONNX, Ray, MLflow

- Data Tools : SQL/NoSQL databases, Vector Databases (FAISS, Pinecone, Weaviate),

Qualifications & Experience :

Education : Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related engineering discipline. Ph.D. preferred for research-intensive roles.

Experience :

- 3- 8 years of experience in Machine Learning, Deep Learning, or AI engineering.

- Hands-on experience with transformer-based architectures, diffusion models, or large-scale language models.

- Proven track record in training, fine-tuning, or deploying generative models in production environments.

- Experience with data pipelines, model optimization, and performance tuning for large-scale AI systems.

Preferred Expertise (Good to Have) :

- Experience in multimodal AI (text + image/video) or speech generation models.

- Knowledge of retrieval-augmented generation (RAG) and knowledge grounding techniques.

- Exposure to vector search, semantic embeddings, and prompt engineering.

- Understanding of AI safety, interpretability, and ethical considerations.

- Contributions to open-source AI projects or published research papers in NLP/CV/GenAI conferences (e.g., NeurIPS, CVPR, ACL).

Soft Skills & Competencies :

- Strong analytical and problem-solving abilities with attention to detail.

- Excellent communication and collaboration skills to work across interdisciplinary teams.

- Creative thinker passionate about emerging AI trends and innovation.

- Ability to manage multiple projects in a fast-paced, research-driven environment