Generative AI Engineer

2 weeks ago


Bengaluru, India Biocliq AI Full time

About the Role We are seeking a motivated Generative AI Engineer with 1–3 years of hands-on experience in building, fine-tuning, and deploying generative models. You will work on real-world applications of LLMs, vision-language models, and multimodal generative systems, contributing to product features, internal automation tools, and customer-facing solutions. This role is ideal for engineers who are passionate about pushing the boundaries of generative AI, experimenting rapidly, and building reliable, production-ready AI features. Key Responsibilities Model Development & Fine-Tuning • Develop, fine-tune, and evaluate LLMs, diffusion models, VLMs, and transformer-based architectures. • Train models using custom datasets, including text, image, audio, and multimodal data. • Implement prompt engineering, instruction tuning, and retrieval-augmented generation (RAG) pipelines. AI System Engineering • Build scalable inference pipelines using Python and modern ML frameworks (PyTorch, HuggingFace, TensorRT, ONNX). • Implement model optimization techniques (quantization, distillation, pruning) for deployment on cloud or edge devices. • Integrate generative AI models into backend microservices and APIs. Data Engineering & Preparation • Create robust data pipelines for dataset collection, preprocessing, augmentation, and evaluation. • Maintain dataset quality with labeling, cleaning, filtering, and benchmarking. Research & Rapid Experimentation • Explore new architectures, training techniques, and SOTA research papers. • Run controlled experiments, A/B tests, and performance validations. • Prototype and iterate quickly to convert ideas into functional demos. Production Deployment • Work with DevOps/MLOps teams to package and deploy models in production (Docker, Kubernetes preferred). • Monitor model performance, drift, quality, latency, and reliability once deployed. Required Skills & Experience • 1–3 years experience in applied ML or generative AI is a MUST • Strong proficiency in Python , PyTorch/TensorFlow, and HuggingFace ecosystem. • Practical experience with LLMs, diffusion models, or vision transformers . • Hands-on experience fine-tuning models on custom datasets. • Familiarity with cloud services (AWS/GCP/Azure) or on-prem GPU stacks. • Understanding of transformer architecture, embeddings, tokenization, attention mechanisms. • Experience with APIs, microservices, or backend engineering using Python (FastAPI/Flask). • Strong debugging, experimentation, and documentation skills. Nice-to-Have • Experience with OpenAI, Anthropic, Mistral, Llama, Qwen , or similar model families. • Familiarity with vector databases (Pinecone, Weaviate, FAISS). • Knowledge of RAG , agentic workflows , and LLM tool-use frameworks (LangChain, LlamaIndex). • Exposure to multimodal models (e.g., CLIP, VLMs, SAM, diffusion models). • Experience in medical imaging, radiology AI, or healthcare AI (bonus for Biocliq/Eliqsar context). • Contributions to open-source ML projects or published research. Education • Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Applied Math, or a related field is a MUST. Equivalent practical experience is also considered.



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