AI-ML Engineer

Role Overview

• Build and iterate on applied AI solutions across ML, GenAI/RAG, and agentic workflows from prototype through production readiness
• Develop data prep and feature engineering pipelines; implement training/finetuning workflows; run rigorous evaluation (offline metrics + human eval where needed)
• Implement AI-backed services and APIs (batch and real-time), including request/response contracts, latency-aware inference, and integration with enterprise systems
• Apply strong software engineering discipline: code quality, unit/integration tests, version control, packaging, and documentation
• Implement core MLOps/LLMOps practices: experiment tracking, model/prompt/version management, reproducible runs, CI/CD hooks, and environment promotion
• Instrument solutions for observability: logging, monitoring, drift/performance tracking, cost telemetry, and incident triage/runbooks
• Embed responsible AI and security guardrails: data handling, access control patterns, prompt injection defenses, PII redaction, and safe output policies
• Collaborate with Solution Architects and client stakeholders to clarify requirements, demo progress, manage tradeoffs, and deliver measurable outcomes
• Lead small workstreams, unblock engineers, conduct code/design reviews, and help establish reusable patterns and accelerators

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related discipline
• 5+ years (Manager) or 7+ years (Director) hands-on experience delivering applied AI solutions (ML and/or GenAI) in production or production-like environments
• Strong Python proficiency and experience building production-grade services, with solid fundamentals in data structures, testing, and debugging
• Experience with ML development: feature engineering, model training, evaluation, and inference (e.g., scikit-learn, XGBoost, PyTorch/TensorFlow—any equivalent stack)
• Experience with GenAI patterns such as RAG (retrieval, chunking, embeddings, reranking) and LLM evaluation (quality, safety, hallucination checks)
• Working knowledge of MLOps/LLMOps concepts: versioning, experiment tracking, CI/CD basics, deployment patterns, and monitoring/drift concepts
• Familiarity with data engineering and modern data platforms (SQL, Spark preferred; warehouses/lakes such as Snowflake/Databricks/BigQuery or equivalent)
• Cloud familiarity (AWS/Azure/GCP) and ability to operate within enterprise constraints (networking basics, IAM concepts, secrets management)
• Strong communication skills; comfortable working directly with client stakeholders in ambiguous environments and documenting decisions and tradeoffs
• Prior consulting industry experience or prior experience in an internal consulting role
• Consistent with the firm’s hybrid work model, this position will require in-person attendance at least two days a week either at a Grant Thornton office or at a client site
• Readiness to travel up to 40%
• Must be currently eligible to work in the United States, position is not eligible for employer sponsorship
• Consistent with the firm’s hybrid work model, this position will require in-person attendance at least two days per week, either at a Grant Thornton office or client site

Tech Stack

• AWS
• Azure
• BigQuery
• Cloud
• Google Cloud Platform
• Python
• PyTorch
• Scikit-Learn
• Spark
• SQL
• Tensorflow

Benefits

• We understand that your needs, responsibilities and experiences are different, and we think that’s a good thing. That’s why we support you with personalized and comprehensive benefits that recognize and empower all the identities, roles and aspirations that make you, well, you. For an overview of our benefit offerings, please visit: https://www.grantthornton.com/careers/rewards-and-benefits
• Benefits for internship positions: Grant Thornton interns are eligible to participate in the firm’s medical, dental and vision insurance programs and the firm’s employee assistance program. Interns also receive a minimum of 72 hours of paid sick leave, and are paid for firm holidays that fall within their internship period.
• Benefits for seasonal employee positions: Grant Thornton seasonal employees are eligible to participate in the firm’s medical, dental and vision insurance programs and the firm’s employee assistance program. Seasonal employees may also be eligible to participate in the firm’s 401(k) savings plan and employee retirement plan in accordance with applicable plan terms and eligibility requirements. Seasonal employees receive a minimum of 72 hours of paid sick leave.
• Grant Thornton employees may be eligible for a discretionary, annual bonus based on individual and firm performance, subject to the terms, conditions and eligibility criteria of the applicable bonus plan or program. Interns and seasonal employees are not eligible for bonus compensation.