Seeking a skilled Machine Learning Engineer to join our AI team. This is a key role in designing, training, fine-tuning, and deploying ML/LLM models that power autonomous exception resolution, anomaly detection, and explainable insights.
The successful candidate will work hands-on with multiple LLM ecosystems such as OpenAI GPT, Anthropic Claude, Google Gemini, and Meta LLaMA. They will 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 such as 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.
Requirements
* Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
* 3+ years building and deploying ML systems.
* English advanced (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.
About Us
We are a leading provider of technology services. Our mission is to deliver business value through technology. We work closely with clients to understand their needs and provide tailored solutions.
UST is an equal opportunities employer. We welcome applications from qualified candidates regardless of their background.