AiLogic Neural Network Pvt Ltd
Website:
ailogic.com
Job details:
AiLogic Neural Network Pvt. Ltd.
Location: Hyderabad, Telangana, India
Work Mode: On-site
Experience: 6+ Years
Employment Type: Full-time
About AiLogic
AiLogic Neural Network Pvt. Ltd. is an AI technology company focused on multilingual translation, custom Transformer architectures, Natural Language Processing (NLP), and Large Language Models (LLMs).
We build and optimize AI systems with a strong focus on research, experimentation, model training, multilingual AI, and production-grade machine learning solutions.
We are looking for a highly technical Lead Data Scientist to lead end-to-end AI/ML model development, provide technical direction, mentor ML engineers, and contribute to the evolution of our AI technology stack.
Important: This is a hands-on AI/ML engineering and research role. Candidates whose experience is primarily in prompt engineering, RAG, or API integration will not be a fit.
Role Overview
As a Lead Data Scientist, you will lead the design, training, fine-tuning, evaluation, and optimization of Transformer-based NLP systems and LLMs.
You will work closely with AI/ML engineers, researchers, and platform teams to develop scalable AI models, with a particular focus on multilingual NLP and machine translation.
The ideal candidate should have strong hands-on experience with Transformer architectures, LLM training, Seq2Seq models, custom datasets, tokenization, model optimization, and GPU-based training.
Key Responsibilities
- Lead the architecture and development of Transformer-based AI and NLP systems.
- Drive technical direction for NLP, LLM, and multilingual AI initiatives.
- Lead training and fine-tuning of Transformer models using large-scale custom datasets.
- Work with Seq2Seq and Encoder-Decoder architectures for machine translation and text generation.
- Optimize models using LoRA, QLoRA, PEFT, quantization, and other optimization techniques.
- Design and improve tokenization pipelines using BPE and SentencePiece.
- Develop and implement model evaluation strategies using BLEU, chrF, perplexity, accuracy, and custom benchmarks.
- Conduct research and experimentation to improve model quality, performance, and efficiency.
- Work with distributed GPU training and optimize training performance.
- Collaborate with platform teams on GPU infrastructure and scalable AI deployments.
- Mentor and technically guide junior and mid-level ML engineers and data scientists.
- Review technical approaches, experiments, code, and model development practices.
- Bridge AI research and production engineering to deliver scalable AI systems.
Required Qualifications
- 6+ years of professional experience in Data Science, Machine Learning, NLP, AI, or a related field.
- Strong hands-on expertise in Transformers, LLMs, Seq2Seq architectures, and Attention Mechanisms.
- Proven experience in training and fine-tuning AI/ML models using custom datasets.
- Strong programming skills in Python.
- Hands-on experience with PyTorch or TensorFlow.
- Strong experience with Hugging Face Transformers.
- Experience with GPU-based model training and optimization.
- Experience with distributed training.
- Strong understanding of NLP model evaluation and performance optimization.
Preferred Qualifications
- Hands-on experience with LoRA, QLoRA, and PEFT.
- Experience with DeepSpeed and/or FSDP.
- Experience with SFT and/or RLHF.
- Experience developing multilingual NLP or Machine Translation systems.
- Experience with large-scale dataset preparation and preprocessing.
- Experience with speech or sequence modeling.
- Research experience or publications in AI/ML/NLP.
- Experience with AWS or cloud-based GPU infrastructure.
- Experience with Docker and Kubernetes.
- Experience with vector databases such as Pinecone or Milvus.
Technical Skills\b
Programming:
Python
Deep Learning:
PyTorch, TensorFlow
NLP / LLM:
Transformers, Hugging Face, LLMs, Seq2Seq, Encoder-Decoder Models, Attention Mechanisms
Fine-Tuning & Optimization:
LoRA, QLoRA, PEFT, Quantization, SFT, RLHF
Training:
DeepSpeed, FSDP, Distributed Training, GPU Optimization
NLP / Language AI:
Machine Translation, Multilingual NLP, Tokenization, BPE, SentencePiece
Evaluation:
BLEU, chrF, Perplexity, Accuracy, Custom Evaluation Benchmarks
Infrastructure:
AWS, GPU Infrastructure, Docker, Kubernetes
Vector / AI:
Pinecone, Milvus, Embeddings, Semantic Search
Not a Fit For This Role
This position is not focused on application-level GenAI development. Candidates whose experience is primarily limited to the following will not be considered:
- Prompt engineering only
- LangChain-only development
- RAG-only application development
- OpenAI/GPT API integration without hands-on model training
- AI chatbot development without core model development
- Low-code GenAI workflows
- API integration without ML/NLP model development experience
What We’re Looking For
We are looking for a hands-on technical leader who can:
- Lead AI/ML research and experimentation independently.
- Design and develop Transformer-based models.
- Take models from experimentation through production.
- Solve complex NLP and model-performance challenges.
- Mentor and technically guide ML engineers.
- Work effectively with large datasets and GPU-based training environments.
- Bridge AI research, model development, and production engineering.
- Take ownership of critical AI initiatives.
Why Join AiLogic?
- Work on advanced multilingual AI and Machine Translation systems.
- Work directly with custom Transformer architectures and LLMs.
- Contribute to AI research, experimentation, and model development.
- Build and optimize AI models rather than working only with third-party APIs.
- Collaborate with AI/ML engineers and researchers.
- Take technical ownership of high-impact AI initiatives.
How to Apply
If you have strong hands-on experience in Transformer models, LLM training, NLP, multilingual AI, Machine Translation, and model optimization, we would like to hear from you.
Please share your updated CV highlighting:
- Transformer / LLM projects
- Model training and fine-tuning experience
- NLP / multilingual AI experience
- GPU / distributed training experience
- Relevant research or publications
- Key AI/ML technologies and frameworks used
Click on Apply to know more.
