Machine Learning Engineer

We are looking for a Machine Learning Engineer II to join Demandbase and build intelligent data and AI/ML capabilities for our Company and Domains Data teams. This is a hands-on ML engineering role requiring a combination of strong ML fundamentals, GenAI/LLM experience, data engineering, and production software engineering. You will build ML solutions from experimentation through production and operate them reliably at enterprise scale. You will work with large-scale first-party (1P) and third-party (3P) data, applying machine learning, NLP, Generative AI, and modern data-processing techniques to improve data intelligence, enrichment, web-signal extraction, and other ML-driven capabilities.

The candidate will have responsibilities across the following functions:

Machine Learning and GenAI:

• Design, develop, and productionize Machine Learning and GenAI solutions for Company and Domain intelligence.
• Build data pipelines and solve data problems using LLMs, transformers, and retrieval/RAG techniques where appropriate.
• Develop ML solutions for problems such as classification, enrichment, information extraction, ranking, and data quality.
• Experiment with models, prompts, embeddings, retrieval techniques, and ML approaches and evaluate them using well-defined quality metrics.
• Collaborate with engineers, analysts, and product managers to translate requirements and data challenges into scalable, production-grade AI/ML solutions.
• Build LLM applications using RAG, semantic retrieval, tool/function calling, and agentic workflows, integrating enterprise APIs and internal data sources.
• Develop reusable AI services, APIs, and orchestration components.

AI Evaluation, Quality and ML Engineering:

• Build scalable data and feature pipelines to process and derive intelligence from large volumes of structured, semi-structured, and unstructured 1P/3P data.
• Develop and maintain evaluation datasets, automated evaluation frameworks, and feedback loops for AI/ML features.
• Define quality metrics and acceptance criteria to assess accuracy, relevance, grounding, latency, reliability, and cost.
• Analyse failure patterns and production performance to continuously improve data, models, prompts, retrieval strategies, and agent behaviour.
• Apply guardrails, grounding, monitoring, and data-quality controls to reduce incorrect or unsupported outputs and improve system reliability.
• Build solutions using Python, SQL, Spark/Pandas, vector search, traditional ML, and LLM-based approaches based on the problem's requirements.

Production AI Engineering and Operational Excellence:

• Build clean, scalable, and maintainable production-grade AI/ML systems.
• Own features end-to-end from design and evaluation to deployment, monitoring, and support.
• Operate cloud-native AI services using Docker, Kubernetes, CI/CD, and observability.
• Monitor and improve quality, reliability, latency, scalability, and cost.
• Maintain versioning for models, prompts, configurations, and evaluations.
• Apply strong engineering principles across architecture, distributed systems, concurrency, performance, unit and integration testing, debugging, and code reviews.

Requirements:

• 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.
• Experience with Scala is a plus.
• 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.

Good to Have:

• 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.