
Biocliq Technologies is an AI healthcare startup focused on revolutionizing clinical diagnostics and treatment. Their solutions utilize advanced algorithms and deep data science research, trained on vast datasets, to provide specific, accurate analyses with actionable insights for error-free clinical decision-making. Their flagship product, UrologiQ, offers detailed reports with quantitative analysis and 3D visualization for diagnosis and treatment, particularly for kidney stone disorders using CT scans. UrologiQ aims to improve diagnosis accuracy, reduce analysis time, and enhance collaborative workflows. The company is ISO 13485 certified and collaborates with companies like Oracle and Nvidia.

Biocliq Technologies is an AI healthcare startup focused on revolutionizing clinical diagnostics and treatment. Their solutions utilize advanced algorithms and deep data science research, trained on vast datasets, to provide specific, accurate analyses with actionable insights for error-free clinical decision-making. Their flagship product, UrologiQ, offers detailed reports with quantitative analysis and 3D visualization for diagnosis and treatment, particularly for kidney stone disorders using CT scans. UrologiQ aims to improve diagnosis accuracy, reduce analysis time, and enhance collaborative workflows. The company is ISO 13485 certified and collaborates with companies like Oracle and Nvidia.
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
• 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.