ashby

Machine Learning Engineer II

demandbase

Remote, IND

Full-time

Remote

Discovered 2 weeks ago

Machine learning engineeringGenerative AILarge language modelsPythonSQLRAG and semantic retrieval

Free

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Key skills for this role

Machine learning engineeringGenerative AILarge language models

Demandbase provides a pipeline AI platform that helps go-to-market teams automate growth and execute account-based strategies.

The company builds data, insights, actions, and outcomes capabilities for B2B enterprises.

Demandbase is seeking a Machine Learning Engineer II to build intelligent data and AI/ML capabilities for its Company and Domains Data teams.

This hands-on role combines ML fundamentals, GenAI and LLM experience, data engineering, and production software engineering.

The role covers ML solutions from experimentation through production and reliable enterprise-scale operation.

Work includes first-party and third-party data, machine learning, NLP, Generative AI, and modern data-processing techniques.

Design, develop, and productionize ML and GenAI solutions for company and domain intelligence.
Build data pipelines and solve data problems with LLMs, transformers, and retrieval or RAG techniques.
Develop solutions for classification, enrichment, information extraction, ranking, and data quality.
Build LLM applications using RAG, semantic retrieval, tool or function calling, and agentic workflows.
Develop reusable AI services, APIs, and orchestration components.

Build scalable data and feature pipelines for large structured, semi-structured, and unstructured datasets.
Develop evaluation datasets, automated evaluation frameworks, and feedback loops for AI/ML features.
Define metrics for accuracy, relevance, grounding, latency, reliability, and cost.
Analyze failures and production performance to improve data, models, prompts, retrieval, and agent behavior.
Apply guardrails, grounding, monitoring, and data-quality controls.

Build clean, scalable, maintainable, production-grade AI/ML systems.
Own features from design and evaluation through 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 engineering practices across architecture, distributed systems, concurrency, performance, testing, debugging, and code reviews.

Five to seven years of experience in Machine Learning Engineering, Applied ML, Data Science Engineering, or a related area.
Strong Python programming experience and good software engineering fundamentals.
Hands-on experience building and productionizing machine learning models or ML-driven applications.
Experience with GenAI and LLM technologies, including APIs or open-source models, embeddings, prompt engineering, and RAG.
Strong knowledge of model training, feature engineering, evaluation, experimentation, and inference.
Experience with NLP, transformers, embeddings, or related unstructured-data techniques.
Strong SQL knowledge and experience working with large datasets.
Working knowledge of at least one cloud platform: AWS, GCP, or Azure.

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

Benefits include medical, personal accident, and term life insurance, along with dental, vision, OPD, mental health, fitness, car lease, and gratuity programs.