Machine Learning Engineer

AiLogic Neural Network Pvt Ltd; Hyderabad; On-site; INR 900,000 - 1,800,000; Full time

Job summary

AiLogic Neural Network Pvt Ltd is seeking a Machine Learning Engineer to design, train, and optimize transformer-based NLP models, with a focus on multilingual translation and production-grade AI systems. You will work with researchers and engineers to build scalable pipelines, experiment with advanced techniques like LoRA/QLoRA, and deploy models across GPU clusters.

This core ML role emphasizes hands-on model development rather than prompt engineering, offering opportunities to publish

Qualifications

• 2+ years in ML/NLP model development with transformer focus.
• Hands-on experience with PyTorch or TensorFlow and Hugging Face.
• Experience training and fine-tuning on large datasets.
• Strong Python programming and software engineering practices.
• Experience with multilingual NLP and model optimization.

Responsibilities

• Design, develop, and optimize transformer-based AI/ML models for NLP.
• Build end-to-end ML pipelines for data processing, model training, evaluation, deployment.
• Train and fine-tune transformer models on large-scale custom datasets.
• Work with Seq2Seq, encoder-decoder architectures, attention mechanisms, and modern LLM architectures.
• Optimize models using LoRA, QLoRA, PEFT, quantization, and distributed training approaches.
• Develop tokenization pipelines using BPE, SentencePiece, and other subword tokenization methods.
• Evaluate model performance using BLEU, perplexity, accuracy, and custom evaluation benchmarks.
• Collaborate with infrastructure teams to manage GPU environments, distributed training, and scalable AI deployments.
• Implement efficient model serving, inference optimization, and production-ready ML solutions.
• Work closely with researchers and engineers to experiment with new architectures, improve existing models, and enhance system performance.

Skills

Transformer architectures

LLMs

Seq2Seq models

Attention mechanisms

Python

PyTorch

TensorFlow

Hugging Face Transformers

GPU acceleration

Distributed training

NLP evaluation methods

Vector databases

Tools

Pinecone

Milvus

Deepspeed

CUDA toolkit

Job description

We are building advanced transformer models focused on multilingual translation and custom transformer architectures. Our team works on large-scale NLP and transformer-based models with a strong focus on research, experimentation, model optimization, and production-grade AI systems.

We are looking for a highly skilled Machine Learning Engineer who is passionate about designing, developing, and optimizing AI/ML models, working with transformer architectures, and contributing to cutting-edge NLP research and development.

This is a core ML engineering role — not a prompt engineering or API integration role.

Role Overview

As a Machine Learning Engineer, you will be responsible for developing, training, fine-tuning, evaluating, and deploying transformer-based NLP models and Large Language Models (LLMs). You will work closely with AI researchers, data scientists, and software engineering teams to build scalable multilingual AI solutions.

The ideal candidate should have strong hands-on experience with ML model development pipelines, data preprocessing, tokenization techniques, transformer architectures, GPU-based training environments, and model optimization techniques. A strong interest in experimentation, improving model performance, and deploying AI systems at scale is essential.

Key Responsibilities

• Design, develop, and optimize transformer-based AI/ML models for NLP and multilingual applications.
• Build and maintain end-to-end machine learning pipelines for data processing, model training, evaluation, and deployment.
• Train and fine-tune transformer models on large-scale custom datasets.
• Work with Seq2Seq, encoder-decoder architectures, attention mechanisms, and modern LLM architectures.
• Optimize models using techniques such as LoRA, QLoRA, PEFT, quantization, and distributed training approaches.
• Develop tokenization pipelines using BPE, SentencePiece, and other subword tokenization methods.
• Evaluate model performance using BLEU, perplexity, accuracy, and custom evaluation benchmarks.
• Collaborate with infrastructure teams to manage GPU environments, distributed training, and scalable AI deployments.
• Implement efficient model serving, inference optimization, and production-ready ML solutions.
• Work closely with researchers and engineers to experiment with new architectures, improve existing models, and enhance system performance.

Required Qualifications

• 2+ years of experience in Machine Learning, Deep Learning, NLP, or AI model development.
• Strong hands-on experience with Transformer architectures, LLMs, Seq2Seq models, and attention mechanisms.
• Proven experience in training and fine-tuning models using custom datasets.
• Strong programming skills in Python and experience with ML development practices.
• Hands-on experience with PyTorch or TensorFlow and Hugging Face Transformers.
• Experience working with vector databases such as Pinecone, Milvus, or similar technologies for embeddings and semantic search.
• Experience with GPU acceleration, distributed training, and NLP evaluation methodologies.
• Understanding of software engineering principles, version control, and production deployment workflows.

Strongly Preferred Skills

• Experience with LoRA, QLoRA, PEFT, and model quantization techniques.
• Knowledge of DeepSpeed, FSDP, or other distributed training frameworks.
• Experience with Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).
• Experience building multilingual NLP systems and machine translation models.
• Knowledge of speech models, sequence modeling, or other advanced AI architectures.
• Contributions to AI/ML research papers, open-source projects, or publications.

This Role Is NOT Focused On

• Prompt engineering as the primary responsibility.
• LangChain-only application development.
• OpenAI/GPT API integrations without hands-on model training.
• Building only RAG pipelines without core model development.
• Low-code or no-code GenAI workflows.
• Python
• PyTorch / TensorFlow
• Hugging Face Transformers
• DeepSpeed / PEFT
• CUDA & GPU Training Environments
• NLP & ML Tooling

What We’re Looking For

• Strong ownership and problem-solving mindset.
• Ability to independently develop and optimize ML models.
• Strong analytical and experimentation skills.
• Deep understanding of transformer architectures and modern NLP systems.
• Ability to bridge AI research with scalable production-grade machine learning systems.
• Passion for building next-generation multilingual AI solutions.