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’s AI/ML engineering standards
Stay current with industry advances and proactively recommend emerging tools, frameworks, and best practices

Bachelor's 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.
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.

Machine Learning Engineer

Key skills