Description
Role : Machine Learning & Generative AI Engineer
Job Location : Bangalore (Hybrid 2-3 days from office)
Experience : 3-7 years
About The Role
We are seeking a highly skilled Machine Learning & Generative AI Engineer to design, build, and deploy advanced ML and GenAI solutions.
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
(ref:hirist.tech)
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