Senior generative ai engineer

3 weeks ago

Hubli, India Ampstek Full time

Title: Senior Generative AI Engineer (Databricks, Data Lake)Location: 100% RemoteJob Type: FulltimeKey Responsibilities• Design and implement Gen AI models (LLMs, multimodal, embeddings, and fine-tuning) for enterprise use cases.• Architect and optimize data pipelines and workflows in Databricks for large-scale AI training and inference.• Manage and maintain data lakes on Databricks, ensuring data quality, governance, and scalability for AI workloads.• Collaborate with data scientists, ML engineers, and product teams to translate business problems into AI-driven solutions.• Implement MLOps pipelines for model deployment, monitoring, and lifecycle management in Databricks.• Explore and evaluate state-of-the-art Gen AI techniques and integrate them into scalable architectures.• Optimize performance, scalability, and cost-effectiveness of AI systems in cloud environments (AWS/Azure/GCP).• Contribute to technical strategy, mentorship, and best practices in AI and data engineering.Required Qualifications• 8+ years of professional experience in AI/ML engineering, data engineering, or related roles.• Strong expertise with Databricks platform, including:o Databricks Workflows & Delta Lakeo Databricks ML Runtime & MLflowo Databricks SQL• Hands-on experience designing and managing data lakes at enterprise scale.• Proficiency in Python (Py Spark, Pandas, ML/AI libraries) and SQL.• Solid background in Machine Learning, Deep Learning, and Generative AI frameworks (e.g., Hugging Face, Lang Chain, Open AI APIs, Tensor Flow, Py Torch).• Experience with MLOps practices, CI/CD pipelines, and cloud-native deployments.• Strong knowledge of cloud platforms (AWS, Azure, or GCP) and data services.• Excellent problem-solving skills and ability to design scalable AI solutions for complex datasets.Preferred Qualifications• Databricks certifications (e.g., Databricks Certified Data Engineer Professional, Machine Learning Professional).• Experience with vector databases (Pinecone, Weaviate, FAISS, Chroma).• Experience with fine-tuning LLMs and integrating RAG (Retrieval-Augmented Generation) pipelines.• Background in data governance, lineage, and compliance in large-scale AI systems.• Contributions to open-source AI/ML projects or publications in the AI

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