Machine Learning Engineer II

The role

You will build and productionize Machine Learning and GenAI solutions for Company and Domain intelligence. This role involves working with large-scale first-party and third-party data using NLP, Generative AI, and modern data-processing techniques.

What you will do

• Design, develop, and productionize Machine Learning and GenAI solutions.
• Build data pipelines and solve data problems using LLMs, transformers, and RAG.
• Develop ML solutions for classification, enrichment, information extraction, ranking, and data quality.
• Experiment with models, prompts, embeddings, and retrieval techniques.
• Collaborate with engineers, analysts, and product managers to translate requirements into AI/ML solutions.
• Build LLM applications using RAG, semantic retrieval, tool/function calling, and agentic workflows.
• Develop reusable AI services, APIs, and orchestration components.
• Build scalable data and feature pipelines for structured and unstructured data.
• Develop and maintain evaluation datasets and automated evaluation frameworks.
• Operate cloud-native AI services using Docker, Kubernetes, CI/CD, and observability.

Required qualifications

• 5–7 years of experience in Machine Learning Engineering, Applied ML, Data Science Engineering, or related areas.
• Strong programming experience in Python and good software engineering fundamentals.
• Hands-on experience building and productionizing machine learning models or ML-driven applications.
• Experience with modern Generative AI and LLM technologies, including LLM APIs or open-source models, embeddings, prompt engineering, and RAG.
• Strong understanding of core ML concepts including model training, feature engineering, model evaluation, experimentation, and inference.
• Experience with NLP, transformers, embeddings, or other techniques for working with unstructured data.
• Strong working knowledge of SQL and experience working with large datasets.
• Working knowledge of at least one cloud platform: AWS, GCP, or Azure.

Nice to have

• Experience with Scala
• Experience with Spark, Kafka, Airflow, or similar large-scale data processing technologies.
• Experience with entity resolution, record linkage, classification, data mining, or data enrichment problems.
• Experience working with large-scale 1P/3P datasets or heterogeneous enterprise data.
• Experience with Kubernetes, Docker, CI/CD, and ML deployment workflows.
• Familiarity with MLOps/LLMOps, experiment tracking, model/prompt versioning, and production ML monitoring.

Benefits

• Group Medical, Personal Accident, and Term Life Insurance
• Preventive healthcare covers dental, vision, and OPD needs
• Mental health support
• Fitness benefit
• Car lease policy
• Gratuity

About the company

Demandbase is a pipeline AI platform that empowers GTM teams to automate growth at scale. The company has offices in San Francisco, New York, Austin, Seattle, India, and the United Kingdom.