Job Description
We are seeking a talented Machine Learning Engineer to join our growing AI team. This role involves designing, training, fine-tuning, and deploying ML/LLM models that power autonomous exception resolution, anomaly detection, and explainable insights.
About the Role
This engineer will work hands-on with multiple LLM ecosystems, implement retrieval-augmented generation (RAG) pipelines, develop prompt engineering and safety techniques, and integrate memory and explainability into agentic workflows.
Key Responsibilities
* Design, train, fine-tune, and deploy ML/LLM models for production.
* Build RAG pipelines using vector databases and frameworks like LangChain, LangGraph, and MCP.
* Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
* Collaborate with data engineering to maintain data pipelines for ML workloads.
* Conduct feature engineering and embeddings generation on structured and unstructured data.
* Implement model monitoring, drift detection, and retraining pipelines.
* Explore emerging LLM/SLM architectures and multi-agent orchestration patterns.
* Cross-functionally collaborate with R&D, data science, product, and engineering teams.
* Mentor junior engineers and contribute to best practices in ML engineering.
Required Qualifications
* Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
* 3+ years building and deploying ML systems.
* English advance (B2).
* Strong Python skills and experience with PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
* Hands-on experience with LLMs/SLMs including fine-tuning, prompt design, and inference optimization.
* Familiarity with at least two of the following: OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA.
* Knowledge of vector databases, embeddings, and RAG pipelines.
* Experience working with both structured and unstructured data at scale.
* Understanding of SQL and distributed data frameworks like Spark or Ray.
* Deep knowledge of ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.
Preferred Qualifications
* Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
* Understanding of AI safety, guardrails, and explainability.
* Hands-on experience deploying ML/LLM solutions on AWS, GCP, or Azure.
* Familiarity with MLOps practices including CI/CD, monitoring, and observability.
* Background in anomaly detection, fraud/risk modeling, or behavioral analytics.
* Contributions to open-source AI/ML projects or research publications.
The right candidate will be passionate about developing and implementing cutting-edge AI technologies.
UST is waiting for you