Description : The role involves working on next-generation AI solutions using modern LLM architectures, deep learning frameworks, and large-scale data pipelines. The selected candidates will engage in building advanced models, optimizing training and inference performance, and deploying models into production environments that handle real-world workloads.

Professionals in this role will collaborate with cross-functional teams, contribute to architectural decisions, mentor junior engineers, and ensure that ML/LLM projects are delivered with high quality, reliability, and scalability.

Key Responsibilities :

- Design, develop, and optimize Large Language Models (LLMs) and other deep learning architectures such as Transformers, encoder-decoder models, and attention-based systems.

- Build scalable training pipelines using frameworks such as PyTorch, TensorFlow, Keras, Scikit-Learn, DeepSpeed, or Hugging Face.

- Work on fine-tuning, pre-training, and domain adaptation of LLMs for various use cases including text generation, summarization, classification, sentiment analysis, and RAG-based retrieval systems.

- Develop robust and efficient ML model training and inference workflows, ensuring low latency, high throughput, and cost-efficient operation.

- Collaborate with data engineering and DevOps teams to deploy LLM and ML pipelines into cloud-based or hybrid production environments.

- Manage, lead, or guide technical teams responsible for delivering ML/LLM models, ensuring milestone achievement and project alignment with business goals.

- Build scalable APIs, model-serving systems, and inference endpoints for high-performance production use cases.

- Conduct research on the latest advancements in LLMs, distributed training, parameter-efficient techniques, quantization, and model optimization.

- Implement best practices in model versioning, experiment tracking, and model lifecycle management.

- Work with large datasets, create preprocessing pipelines, and perform efficient feature extraction and embedding generation.

- Ensure production-grade reliability, monitoring, observability, and continuous improvement of deployed models.

Required Skills :

Machine Learning & Deep Learning :

- Strong foundation in ML algorithms, deep learning architectures, NLP techniques, and model evaluation methodologies.

- Hands-on experience with modern architectures including Transformers, Encoder-Decoder models, and LLMs.

Frameworks & Tools :

Proficiency with at least two of the following :

- PyTorch

- TensorFlow

- Keras

- Hugging Face Transformers

- Scikit-Learn

- DeepSpeed

Programming & Engineering :

- Strong expertise in Python for ML model development, automation, and pipeline creation.

- Experience working with distributed training, GPU-based model development, and optimization techniques.

Production & Deployment :

- Experience deploying ML/LLM models in production environments using cloud-native tools or scalable architecture patterns.

- Understanding of CI/CD processes, API development, model serving frameworks, and monitoring tools.

Leadership & Collaboration :

- Ability to manage or mentor teams, provide technical guidance, and oversee end-to-end ML delivery cycles.

- Strong problem-solving, analytical thinking, and cross-team collaboration skills.

Preferred Experience (Good to Have) :

- Experience working with RAG systems, vector databases, and embeddings.

- Exposure to MLOps platforms and workflow systems.

- Experience with optimization libraries, quantization techniques, and multi-GPU training setups.

- Research or publication experience in LLM, NLP, or deep learning fields.

Who Should Apply :

Candidates who can work independently, bring clarity to complex problem statements, and take ownership of delivering high-performance ML/LLM solutions in a distributed team environment. Individuals who continuously stay updated with the latest LLM research and enjoy working on challenging, large-scale AI systems will excel in this role.