Generative AI Engineer

1 week ago

Bengaluru, Karnataka, India Biocliq AI Full time ₹ 80,00,000 - ₹ 1,60,00,000 per year

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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