About You
You are a highly skilled Machine Learning Engineer who thrives on building real-world AI solutions that deliver measurable business impact. You enjoy working with modern ML and LLM technologies, designing scalable training and inference pipelines, and ensuring models are reliable, explainable, and production-ready.
You communicate clearly, collaborate effectively with engineering and product teams, and stay ahead of emerging AI advancements. You are also someone who enjoys mentoring others and contributing to the organization鈥檚 AI/ML best practices.
You bring to Applaudo the following competencies:
- Bachelor鈥檚 Degree or higher in Computer Science, Computer Engineering, Data Science, or related field or equivalent experience.
- Strong expertise with the Python ML stack: scikit-learn, pandas, NumPy.
- Hands-on experience integrating and optimizing LLM-based systems (OpenAI API, LangChain, fine-tuning workflows, RAG pipelines).
- Solid knowledge of forecasting models: regression, time-series, probabilistic approaches.
- Experience building and maintaining confidence scoring systems using hybrid rule-based + ML methods.
- Deep understanding of model evaluation, bias detection, drift analysis, and performance analytics.
- Ability to design and implement training, inference, and continuous-improvement pipelines.
- Familiarity with MLOps practices, including model versioning, monitoring, and retraining cycles.
- Experience with vector databases such as FAISS, Pinecone, Weaviate and HuggingFace Transformers.
- Strong analytical, problem-solving, and system-thinking abilities.
- Excellent communication and documentation skills for collaborating across technical and non-technical teams.
- Experience mentoring engineers or leading ML initiatives. (Nice to Have)
- Cloud experience with AWS, Azure, or GCP; familiarity with distributed data processing. (Nice to Have)
- Advanced English level, as you will collaborate with teams and stakeholders across regions.
You will be accountable for the following responsibilities:
- Design, build, and maintain training and inference pipelines for traditional ML and LLM-based systems.
- Develop predictive models using regression, time-series, and probabilistic techniques.
- Build and refine confidence scoring systems to ensure model reliability.
- Integrate LLMs (OpenAI, HuggingFace) across products using APIs, LangChain, fine-tuning, and RAG pipelines.
- Conduct bias, drift, error, and fairness analysis to maintain model transparency and robustness.
- Implement and manage vector databases to support retrieval-augmented generation pipelines.
- Collaborate closely with engineering and product teams to bring ML-powered features into production environments.
- Apply MLOps principles: model monitoring, retraining workflows, versioning, and CI/CD for ML.
- Provide mentorship to team members and contribute to shaping the company鈥檚 AI/ML engineering standards.
- Stay current with industry advances and proactively recommend emerging tools, frameworks, and best practices.