AI Engineer - 1

We are looking for a highly motivated AI/ML Engineer with strong expertise in Natural Language Processing (NLP), Machine Learning, and Large Language Models (LLMs). The ideal candidate will have hands-on experience in developing, fine-tuning, and deploying transformer-based models and possess strong computer science fundamentals. You will work closely with research and engineering teams to build intelligent AI products, optimize model performance, and contribute to the development of next-generation language AI solutions.

The core responsibilities for the job include the following:

NLP and LLM Development:

• Design, develop, and optimize NLP and Generative AI solutions using modern transformer architectures.
• Fine-tune pre-trained language models for domain-specific tasks such as: Text classification, Information extraction, Question answering, Summarization, Conversational AI, and Semantic search and retrieval.
• Build custom machine learning and deep learning models for NLP applications.
• Evaluate model performance using appropriate metrics and experimentation frameworks.

Model Training and Optimization:

• Fine-tune open-source foundation models and transformer-based architectures.
• Develop training pipelines for supervised, semi-supervised, and instruction-tuning workflows.
• Improve model performance through hyperparameter tuning, prompt optimization, and dataset engineering.
• Work with embeddings, vector databases, retrieval systems, and RAG-based architectures.

Engineering and Deployment:

• Build scalable model inference and serving pipelines.
• Develop APIs and backend services using Python.
• Optimize models for latency, throughput, and production deployment.
• Collaborate with platform and engineering teams to deploy AI solutions in cloud environments.

Research and Innovation:

• Stay current with advancements in NLP, LLMs, transformers, and Generative AI.
• Experiment with new architectures, fine-tuning approaches, and model evaluation techniques.
• Contribute to technical discussions, architecture reviews, and knowledge-sharing initiatives.

Requirements:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, Related quantitative disciplines.
• Preferred institutions: IITs, IISc, IIITs, BITS Pilani, Top NITs.
• 1-3 years of hands-on experience in Machine Learning, NLP, or AI Engineering roles.
• Demonstrated experience building and fine-tuning NLP or transformer-based models.
• Strong problem-solving and analytical skills.
• Ability to learn and adapt quickly in a fast-moving AI landscape.
• Excellent communication and collaboration skills.
• Passion for building impactful AI products and solving real-world problems.
• Target Talent Pool: IITs, IISc, IIITs, BITS Pilani, Top NITs, and other leading engineering institutions with strong AI/ML backgrounds.

Technical Skills:

• Programming: Strong proficiency in Python. Experience with software engineering best practices, debugging, and code optimization.
• Machine Learning and Deep Learning: Strong understanding of: Supervised and unsupervised learning, Deep learning fundamentals, Model evaluation and validation, Feature engineering, and Optimization techniques.
• NLP: Solid understanding of: Tokenization, Embeddings, Attention mechanisms, Language modeling, Sequence-to-sequence learning, Information retrieval, and NLP evaluation metrics.
• Transformers and LLMs: Strong knowledge of transformer architectures including BERT, RoBERTa, T5 GPT-family models, and Llama-family models. Experience with: Fine-tuning, Instruction tuning, Parameter-efficient tuning (LoRA, QLoRA), Model evaluatio, Prompt engineering.
• Frameworks and Tools: Experience with: PyTorch, Hugging Face Transformers, NumPy, Pandas, Scikit-learn. Familiarity with: LangChain or equivalent orchestration frameworks, Vector databases, Model serving frameworks, and Experiment tracking tools.
• Computer Science Fundamentals: Strong understanding of: Data Structures and Algorithms, Object-Oriented Programming, Operating Systems, Distributed Systems fundamentals, Database concepts, and API development.

Preferred Qualifications:

• Experience working with production-grade AI systems.
• Familiarity with Retrieval-Augmented Generation (RAG).
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Exposure to MLOps practices and CI/CD workflows.
• Contributions to open-source projects, research publications, or Kaggle competitions.
• Experience with agentic AI systems and multi-agent workflows.