This role provides the opportunity to work on cutting-edge AI technologies such as LLM fine-tuning, Transformer architectures, Retrieval-Augmented Generation (RAG), and enterprise-scale ML systems.

The ideal candidate will combine strong technical expertise with innovative thinking to deliver impactful AI solutions aligned with business needs.

Required Skills & Qualifications :

- 3 to 7 years of experience in Machine Learning, Deep Learning, and AI model development

- Strong proficiency in Python and ML frameworks : PyTorch, TensorFlow, Scikit-Learn, MLflow

- Expertise in Transformer architectures (BERT, GPT, T5, LLaMA, Falcon, etc.) and attention mechanisms

- Hands-on experience with Generative AI : LLM fine-tuning (LoRA, QLoRA, PEFT, full model tuning), instruction tuning, and prompt optimization

- Experience with RAG pipelines embeddings, vector databases (FAISS, Pinecone, Weaviate, Chroma), and retrieval workflows

- Strong foundation in statistics, probability, and optimization techniques

- Proficiency with cloud ML platforms (Azure ML / Azure OpenAI, AWS SageMaker / Bedrock, GCP Vertex AI)

- Familiarity with Big Data & Data Engineering : Spark, Hadoop, Databricks, SQL/NoSQL databases

- Hands-on experience with CI/CD, MLOps, and automation pipelines (Airflow, Kubeflow, MLflow)

- Experience with Docker, Kubernetes for scalable ML/LLM deployment

Preferred Qualifications :

- Experience in NLP & Computer Vision (Transformers, BERT/GPT models, YOLO, OpenCV)

- Knowledge of vector search & embeddings for enterprise-scale GenAI solutions

- Exposure to multimodal AI (text + image/video/audio) and Edge AI / federated learning

- Familiarity with RLHF (Reinforcement Learning with Human Feedback) for LLMs

- Understanding of real-time ML applications and low-latency model serving

Key Responsibilities :

- Design, build, and deploy end-to-end ML pipelines including data preprocessing, feature engineering, model training, and deployment

- Develop and optimize LLM-based solutions leveraging Transformer architectures for enterprise applications

- Implement RAG pipelines using embeddings and vector databases to integrate domain-specific knowledge into LLMs

- Fine-tune LLMs on custom datasets (text corpora, Q&A, conversational data, structured-to-text) for specialized tasks

- Ensure scalable deployment of ML & LLM models on cloud environments with monitoring, versioning, and performance optimization

- Collaborate with data scientists, domain experts, and software engineers to deliver AI-driven business impact

What Were Looking For :

Beyond technical skills, the ideal candidate should excel in :

- Real-world AI application & deployment

- Prompt engineering & model evaluation

- Fine-tuning & customization of LLMs

- Understanding of LLM internals & ecosystem tools

- Creativity, problem-solving & innovation

- Business alignment, security, and ethical AI practices