AI, Data & Technology - AI-ML Engineer

Grant Thornton US is hiring AI-ML Engineers to design and build applied AI solutions—ranging from ML models to GenAI/RAG and agentic workflows—while operating with strong software engineering discipline and foundational MLOps/LLMOps practices. Hiring in most major US cities. 

As an AI-ML Engineer you will contribute across the lifecycle: feature engineering, model development and evaluation, API/service implementation, and production readiness (versioning, testing, basic CI/CD, monitoring concepts, and responsible AI guardrails), working closely with Solution Architects and client stakeholders to deliver outcomes. We are actively recruiting for: Manager level (3–4 years) for engineers who can lead small workstreams and coach others, and Director level (5 years) for senior engineers who can set technical standards, drive quality and reliability, and help shape reusable patterns and accelerators. Ideal candidates are intellectually curious, comfortable working in ambiguous client environments, and motivated to build AI solutions that are accurate, secure, cost-aware, and production-ready. This role is specifically a strategic investment to grow our AI capabilities and advisory services.

If you want your work to matter, this is the moment: we are not building “another AI consulting practice”—we are rewriting the playbook for how clients deliver on the promise of AI. We’re building a practice where teams love the pace, the craft, and the real-world impact.

Day-to-day responsibilities:

• 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 

You have the following technical skills and qualifications:

• 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 4 0%
• 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

  The base salary range for this position is between $120,000 and $274,554. Placement within the pay range is at Grant Thornton’s discretion, and it is based on multiple factors, including but not limited to, job -related knowledge/skills, experience, business needs, progression within the role, geographic location, and internal equity. At Grant Thornton, compensation decisions are dependent upon the facts and circumstances of each position and candidate. 

Salary : $120,000 - $274,554