Applied AI & Machine Learning Engineer

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MidZdalnieFull-time

#472717·Dodano 2 dni temu·4

Źródło: SPD Technology

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Tech Stack / Keywords

Python PyTorch Scikit-LearnspaCyLangChain LangGraphLangSmithTensorFlow SQL NLP

Podsumowanie oferty

Develop machine learning and generative AI solutions for the PitchBook Platform, collecting high-volume data and surfacing insights for capital markets. Build and deploy transformer-based models, NLP pipelines, and scalable ML systems using tools including Python, PyTorch, LangChain, and LangGraph.

Wymagania

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field.
4+ years of production experience in ML, including developing transformer-based models and deploying them in scalable systems.
Proficiency in Python and machine and deep learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, or spaCy.
Experience with natural language processing and modern LLM-based techniques.
Experience with the full ML lifecycle, including data preprocessing, training, evaluation, and monitoring.
Skills in SQL and data extraction methods.
Ability to communicate and collaborate with team members, engineering teams, and stakeholders.

Nice to have:

Exposure to financial services, investment banking, venture capital, or private equity.

Obowiązki

Design, develop, and fine-tune generative AI models using transformer architectures.
Build automated pipelines and intelligent agents with LangChain, LangGraph, and LangSmith, integrating vector databases for similarity search and real-time analysis.
Process and analyze unstructured data using embeddings and statistical modeling to identify insights.
Collaborate with cross-functional teams to create production-grade ML systems for real-time, high-volume data processing.
Mentor junior team members and stay updated on trends in Agentic AI, transformer models, and generative systems.
Work with Product and Engineering teams to improve data management and support innovation; present analyses clearly.
Build NLP pipelines with PyTorch, Scikit-Learn, and spaCy; implement vector databases and develop hybrid models to extract insights from diverse data sources.

Benefity

Flexible working schedule.
Mentorship program and personal development plan.
Individual learning through the corporate library.
Public speaking support.
Company-wide tech and cultural events.

Elastyczne godziny

Spotkania integracyjne

Inne informacje

Recruitment process: recruiter pre-screening, technical interview (up to 90 minutes), system design interview (up to 45 minutes), and final interview with the Engineering Manager (60 minutes).

SPD Technology

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