Machine Learning & Generative AI Engineer

We are looking for a Machine Learning & Generative AI Engineer to design, build, and deploy advanced ML and GenAI solutions
This role offers an exciting opportunity to work on cutting-edge AI systems, including LLM fine-tuning, Transformer architectures, and RAG pipelines, while also driving traditional ML model development for decision-making and automation, Key Skills Required 37 years in Machine Learning, Deep Learning, and AI model development Strong proficiency in Python, PyTorch, TensorFlow, Scikit-Learn, MLflow Expertise in Transformer architectures (BERT, GPT, T5, LLaMA, Falcon, etc) and attention mechanisms Experience with Generative AI: LLM fine-tuning (LoRA, QLoRA, PEFT, full model tuning), instruction tuning, and prompt optimization Hands-on with RAG (Retrieval-Augmented Generation) embeddings, vector databases (FAISS, Pinecone, Weaviate, Chroma), and retrieval workflows Strong foundation in statistics, probability, and optimization techniques Experience with cloud ML platforms: Azure ML / Azure OpenAI, AWS SageMaker / Bedrock, or GCP Vertex AI Familiarity with Big Data & Data Engineering: Spark, Hadoop, Databricks, SQL/NoSQL databases Proficiency in CI/CD, MLOps, and automation pipelines (Airflow, Kubeflow, MLflow) Hands-on with Docker, Kubernetes for scalable ML/LLM deployment Good to Have NLP & Computer Vision: Transformers, BERT/GPT models, YOLO, OpenCV Experience with vector search & embeddings for enterprise-scale GenAI solutions Exposure to multimodal AI (text + image/video/audio) and Edge AI / federated learning Knowledge of RLHF (Reinforcement Learning with Human Feedback) for LLMs Familiarity with real-time ML applications and low-latency model serving Role Responsibilities Design, build, and deploy end-to-end ML pipelines data preprocessing, feature engineering, model training, and deployment Develop and optimize LLM-based solutions for enterprise use cases, leveraging Transformer architectures 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 domain-specific tasks Ensure scalable deployment of ML & LLM models on cloud environments with monitoring, versioning, and performance optimization Collaborate with cross-functional teams (data scientists, domain experts, software engineers) to deliver AI-driven business impact