Headcount: Approximately 373 employees (site snapshot)
Location: Headquartered in Lisbon, Portugal with additional delivery hubs
Business: People-first nearshore IT consulting and dedicated software development teams
Proprietary products: Keywork platform and Aura assistant
Revenue (reported): Approximately €17M (company update Jan 2024)
Company Overview
Problem Domain
Scaling engineering capacity and delivering software/IT consulting via nearshore teams
Founded
2012
Industry
IT consulting / Nearshore software development
Funding Track Record
Funding
Founders
What we do
Join the Team
Senior Machine Learning Engineer
RemoteRemote (Canada), CA
Remote • Remote (Canada), CA
Who you are
Teeming tracks opportunities at over 24,000 AI startups, then works with you to find (and land) the one you'll love.
Backend Developer
Part-timeMunich, DE
Part-time • Munich, DE
Software Engineer
InternshipManchester, GB
Internship • Manchester, GB
Backend Developer
InternshipCambridge, GB
Internship • Cambridge, GB
Backend Developer
Part-timeAmsterdam, NL
Part-time • Amsterdam, NL
AI Researcher
Part-timeNovi Sad, RS
Part-time • Novi Sad, RS
DevOps Engineer
InternshipUtrecht, NL
Internship • Utrecht, NL
5+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production, with a focus on ML Engineering and ML Ops
Experience in the following technical areas:
Architecture design for distributed systems
Building maintainable and testable codebases and services
Developing ML systems, such as recommendation systems and information retrieval solutions
Relational DB, non-relational DB, and vectorDB
Developing AI applications powered by LLMs and/or agents
Proficient in building services in Python and common ML frameworks, such as PyTorch and Scikit-learn
Experience with observability tools for both online and offline evaluation and tracing for AI applications
Demonstrated experience working with large datasets, and are comfortable with high-scale data ingestion, transformation, analysis, and prediction tools
Experience working collaboratively with engineering, product, data, and other cross-functional teams
Experience with developing machine learning applications at scale, from inception to driving user impact
Familiar with serving machine learning models for streaming and batch inference at scale
Experience with packaging, CI/CD and pipeline automation
Good understanding of machine learning systems’ testing, benchmarking, and online monitoring
Experience with dataset engineering, including data curation, augmentation, and synthesis, to assist ML model improvement
What the job involves
This role is part of the AI Insights team, which owns the services that power Affinity's leading relationship intelligence, which is achieved through information retrieval from billions of unstructured and structured data points
As a Senior Machine Learning Engineer, you will collaborate with data engineers, software engineers, and product managers to shape the future of private capital's leading CRM platform
In this role, you will design and build AI systems that efficiently uncover insights from compelling business interaction data – an exciting and unique opportunity within the industry
This is an applied machine learning position with a strong emphasis on engineering, not research. You will play a key role in advancing our ML Ops and ML Engineering capabilities
Take projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation
Gather product requirements and translate them into robust ML system design requirements
Work on a variety of information extraction, information storage and information retrieval problems from both structured and unstructured data sources
Architect efficient and scalable systems to support serving inference services
Develop reusable AI-based services — including ML models, foundation models, and agents — that will be consumed by other backend services at Affinity
Lead complex technical initiatives and provide technical input for peers through system design reviews, code reviews, and promoting best practices
Collaborate with cross-functional teams (product, infra, data engineer, and software engineer) and ML engineering teams to build robust, high-scale systems that underlie all of our data processing and ML Operations
Tech stack: Our ML pipeline manages multiple Python services that support various AI features, including utilizing OCR to extract information from unstructured data, serving embedding models to vectorize chunks, and ranking a list of recommendations based on relevance and user preference. We maintain the Docker images and deployment pipeline via CircleCI. Our data stack includes Spark, Python, Kafka, and Databricks
