AI/ML Engineer - NLP

AI/ML Engineer NLP Focus

Location : Noida / Gurugram

Type : Full-time

Hiring for Our Client

About The Role

Key Responsibilities

• Model Development & Fine-tuning :
• Customize, fine-tune, and optimize state-of-the-art NLP models (e.g., Transformer-based, Conformer-based, VITS).
• Train models from scratch for business-specific challenges.
• Generative AI & Prompt Engineering :
• Design effective prompts tailored to user and business needs.
• Build similarity models (e.g., BERT, XLNet, cross-encoders) for LLM-based Retrieval-Augmented Generation (RAG).
• Model Deployment & Optimization :
• Deploy models at scale using Docker, Kubernetes, and AWS.
• Optimize performance using vLLM (Paged Attention), quantization, and adapter-based tuning (LoRA, QLoRA).
• Performance Monitoring & Pipeline Development :
• Evaluate and enhance model performance (accuracy, latency, scalability).
• Build robust ML pipelines using tools like Airflow and AWS Batch.
• Cross-functional Collaboration :
• Work closely with data scientists, engineers, and product teams to integrate NLP models into production systems.
  

Required Skills & Experience

• Experience : 6+ years in NLP/ML with deep expertise in fine-tuning and transfer learning.
• NLP Specialization : ASR, TTS, or custom LLM architectures (e.g., auto-regressive transformers, RWKV).
• Generative AI : Skilled in designing prompts and leveraging LLMs for business use cases.
• Programming : Strong in Python; experience in model implementation and pipeline building.
• Deployment : Hands-on with Docker, Kubernetes, and cloud platforms (AWS preferred).
• Frameworks & Tools : TensorFlow, PyTorch, Hugging Face, NLTK, SpaCy, Gensim.
  

Bonus Skills (Nice To Have)

• Cloud & MLOps : AWS Lambda, Step Functions, Batch, GCP, Azure, or Nemo.
• Scalability : Experience in managing and scaling large AI deployments in production.
  

Preferred Qualifications

• Bachelor's or Masters in Computer Science, Data Science, Engineering, or a related field.
• Experience working with large datasets and building robust preprocessing pipelines.
• Strong grasp of neural networks and advanced deep learning architectures for NLP.