Machine Learning

6 days ago

Bengaluru, Karnataka, India emagine Full time US$ 1,25,000 - US$ 1,75,000 per year

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

  • 3–7 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

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