AI/ML Engineer

UST is hiring an AI/ML Engineer to develop, evaluate, and deploy enterprise AI, machine learning, and intelligent automation solutions. The role spans rapid prototyping and customer problem-solving, reusable architectures and measurement frameworks, and production-grade generative AI, agentic systems, and machine learning applications.

Responsibilities:

• As an AI/ML Engineer, you will operate across several interconnected modes
• Firefighter Mode When an urgent or ambiguous AI challenge emerges, you bring structure, speed, and technical judgment. Rapidly evaluate new AI tools, models, frameworks, and platforms for enterprise use cases
• Build working prototypes and technical proofs of concept under compressed timelines. Investigate AI applications that are underperforming and identify issues across data, prompts, retrieval, orchestration, models, infrastructure, or user workflows
• Validate claims related to productivity improvement, automation, quality gains, cost reduction, and time savings. Instrument AI applications to determine whether they are being used, whether they are working, and whether they are creating measurable value
• Recommend whether an AI initiative should be scaled, redesigned, paused, or discontinued. Communicate findings clearly to technical teams, business stakeholders, customers, and senior leadership
• Step into high-priority customer and delivery situations, reduce ambiguity, and drive them toward closure
• Systems Thinker Mode Design reusable architectures for enterprise Generative AI, Agentic AI, and Machine Learning applications
• Build measurement frameworks that work across AI use cases such as software engineering, analytics, customer support, recruiting, HR, supply chain, finance, insurance, and healthcare
• Define outcome metrics covering adoption, productivity, task completion, quality, accuracy, latency, cost, user satisfaction, risk, and business value
• Design data pipelines and instrumentation that capture agent traces, tool calls, token consumption, model performance, user activity, feedback signals, and workflow outcomes
• Build dashboards, semantic models, evaluation workspaces, and self-service analytics that allow stakeholders to understand AI performance without depending on manual reporting
• Establish standardized evaluation and observability patterns across a fragmented and rapidly changing AI technology landscape
• Help shape how UST and its clients think about AI-enabled productivity, workforce augmentation, operating models, job responsibilities, and organizational transformation
• Convert successful prototypes into reusable accelerators, reference architectures, and enterprise platforms
• Builder Mode Develop Generative AI and Machine Learning applications that solve complex enterprise and customer problems. Build intelligent agents and multi-agent workflows with planning, reasoning, tool use, memory, context management, human review, and workflow orchestration
• Design production-grade Retrieval-Augmented Generation systems, including document ingestion, parsing, chunking, embedding generation, indexing, hybrid retrieval, reranking, grounding, citation generation, and hallucination mitigation
• Develop secure Python services and APIs using FastAPI, Flask, or comparable frameworks. Integrate Large Language Models with enterprise databases, APIs, document repositories, SaaS platforms, knowledge bases, and business applications
• Build scalable pipelines for data preparation, training, fine-tuning, inference, evaluation, deployment, monitoring, and continuous improvement
• Develop Machine Learning and Deep Learning solutions across natural language processing, computer vision, forecasting, classification, recommendation, and structured-data use cases
• Fine-tune and adapt models using techniques such as PEFT, LoRA, instruction tuning, prompt tuning, and domain-specific optimization when justified
• Apply prompt engineering and context engineering techniques to improve accuracy, consistency, safety, and task completion
• Deploy AI applications using cloud-native architectures, containers, microservices, event-driven systems, and managed AI platforms
• Ensure that AI systems meet enterprise requirements for security, privacy, reliability, scalability, governance, auditability, and Responsible AI

Requirements:

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field, or equivalent practical experience
• Three or more years of hands-on experience in Machine Learning, Artificial Intelligence, Data Engineering, Software Engineering, Generative AI, or a related area
• Strong proficiency in Python and experience building production-quality software
• Experience developing and deploying Machine Learning, Generative AI, or intelligent automation applications
• Experience working with Large Language Models, AI agents, RAG systems, or modern AI development platforms
• Strong understanding of Machine Learning fundamentals, model evaluation, data preparation, experimentation, and production deployment
• Ability to take an AI use case from an ambiguous initial idea through architecture, implementation, evaluation, and deployment
• Strong written and verbal communication skills, including the ability to present technical findings to leadership and nontechnical stakeholders
• Demonstrated ownership, sound judgment, and the ability to move quickly without compromising critical quality, security, or governance requirements