• Own the full ML lifecycle from data collection to ongoing quality assessment. • Train CV/ML models to detect deepfakes and uncover complex anomaly patterns. • Balance research and speed in model building and heuristic solutions. • Serve models efficiently with FastAPI and PyTorch, integrating with Java backend. • Build and monitor ML pipelines and dashboards with tools like Airflow and ClickHouse.

Role Description - Design, develop, and deploy end-to-end ML and AI pipelines, from problem framing and experimentation through to production. - Build and maintain robust data and ML pipelines for model training, evaluation, and deployment, develop LLM-powered features including RAG, structured prompting, few-shot learning, and OCR integration. - Implement and evolve ML/LLMOps practices: model versioning, production monitoring, reproducibility, and continuous improvement. - Develop APIs and microservices to expose ML/AI models to business applications. - Analyze large datasets, identify modeling challenges, propose solutions, and iterate quickly. - Collaborate closely with Data, Product, and Engineering teams to turn prototypes into scalable, reliable production solutions. - Stay current on academic and industrial advances in Machine Learning, LLMs and emerging AI technologies and apply them to real-world use cases. - Contribute to best practices in testing, CI/CD, and deployment automation. Qualifications - You hold a Master's degree (or equivalent) in Computer Science, Applied Mathematics, Data Science, or a related quantitative field. - You have 3 to 5 years of experience developing and deploying Machine Learning models in production environments. - You are proficient in Python and have hands-on experience with major ML/AI frameworks (scikit-learn, XGBoost, LightGBM, HuggingFace Transformers, DSPy…), valuing practical experience and adaptability over specific tools. - You have solid knowledge of ML/LLMOps tools (MLflow, Kubeflow, DVC, DeepEval or similar) and ML-oriented DevOps practices. - You have experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) and containerization (Docker, Kubernetes). - You are comfortable with SQL and knowledgeable in feature engineering and data preprocessing best practices. - You have a pragmatic, analytical, and results-oriented mindset: favoring frequent iterative deliveries over long research tunnels. - You enjoy working in cross-functional teams (Data, Tech, Product) and can translate business needs into concrete ML/AI solutions. - You are autonomous, curious, and comfortable in a fast-evolving ecosystem. - Professional proficiency in English, bilingual in French. Requirements - Package: 55.000 Euro to 70.000 Euro - fix salary only. Benefits - Join a mission-driven company with a concrete impact on the new generation. - Share our three core virtues with our people all over Europe: #Committed, #OneTeam, #DrivEn. - Take part in a dynamic internal life, where everyone can contribute (all-hands, local parties, team drinks, team-buildings, etc.). - Grow and develop your career with a strong training policy, including an annual individual budget for learning opportunities (95% of our employees were trained last year!). - Enjoy our flexible work environment: remote-friendly, with one month per year from anywhere in Europe, and home office equipment support. Some positions can even be fully remote. - Benefit from our Family Care Policy: 4 additional paid days off during your return month from parental leave, 3 paid child-sick days per year, and other initiatives to support parents. - Take care of your mental health with Moka Care: 4 free therapy or coaching sessions per year (with additional support in challenging personal situations), plus regular workshops for managers in partnership with Moka Care and Alan. - Make your voice count: our Take Care Survey (run twice a year) ensures employee feedback directly shapes JobTeaser’s culture, collaboration and cross-team work. - Protect your focus time with our No-Meeting Wednesday mornings initiative. - Collaborate closely with our CSE, actively involved in enhancing work life and benefits (with the HappyPal platform). - Enjoy the essentials: RTT, health insurance, lunch vouchers, holiday bonus, and 50% public transport reimbursement. - A modern and well-located office at WeWork - including delicious barista coffee and free yoga lessons. - Be proud: 88% of our employees say they are proud to work in their team at JobTeaser (Internal Take Care Survey, November 2024).

• Design, build, and deploy AI/ML solutions with a focus on large language models (LLMs) using Python and AWS infrastructure. • Architect scalable APIs and microservices utilizing FastAPI, ensuring robust, high-performance systems. • Integrate LangChain and advanced AI frameworks to deliver innovative model interaction capabilities. • Collaborate with cross-functional teams to translate complex mathematical, statistical, or physics-based problem statements into actionable engineering solutions. • Apply core math, statistics, or physics knowledge to improve model performance, interpretability, and reliability. • Maintain high standards of code quality, documentation, and peer reviews within a fast-paced core team. • Communicate technical concepts and project progress clearly through both written reports and verbal discussions.

Role Description This position is needed to scope, design, and deploy machine learning systems into the real world. The individual will closely partner with Product & Engineering teams to execute the roadmap for Twilio’s AI/ML products and services. You will understand customers' needs, build data products that work at a global scale, and own end-to-end execution of large-scale ML solutions. To thrive in this role, you must have a deep background in ML engineering and a consistent track record of solving data & machine-learning problems at scale. You are a self-starter, embody a growth attitude, and collaborate effectively across organizations. Responsibilities - Build and maintain scalable machine learning solutions in production - Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness - Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems - Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements, and define the scope of the systems needed - Work closely with data platform teams to build robust scalable batch and real-time data pipelines - Collaborate with software engineers, build tools to enhance productivity and to ship and maintain ML models - Drive high engineering standards on the team through mentoring and knowledge sharing - Uphold engineering best practices around code reviews, automated testing, and monitoring Qualifications - 7+ years of applied ML experience with proficiency in Python - Strong background in the foundations of Machine Learning and building blocks of modern Deep Learning - Track record of building, shipping, and maintaining Machine Learning models in production in an ambiguous and fast-paced environment - Track record of designing and architecting large scale experiments and analysis to inform product roadmap - Clear understanding of frameworks like PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do - Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring - Demonstrated ability to ramp up, understand, and operate effectively in new application/business domains - Explored modern data storage, messaging, and processing tools (Kafka, Apache Spark, Hadoop, Presto, DynamoDB etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar - Experience working in an agile team environment with changing priorities - Experience of working on AWS Desired - Experience with Large Language Models Location This role will be remote and based in India. Travel For this role, you may be required to travel occasionally to participate in project or team in-person meetings. Benefits - Competitive pay - Generous time off - Ample parental and wellness leave - Healthcare - Retirement savings program - And much more (offerings vary by location)