AI-ML Engineer

Grant Thornton (US)

AI-ML Engineer responsible for designing and building applied AI solutions. Collaborating with teams to ensure production readiness and implementing strong software engineering practices.

Posted 4/25/2026full-timeHouston • Texas • 🇺🇸 United StatesMid-LevelSenior💰 $120,000 - $274,554 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudGoogle Cloud PlatformPythonPyTorchScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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
Must be currently eligible to work in the United States, position is not eligible for employer sponsorship.

Benefits

Comp & perks

Health insurance
401(k) matching
Paid time off
Flexible working arrangements
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Pythonfeature engineeringmodel trainingmodel evaluationinferenceMLOpsLLMOpsCI/CDdata structuresdebugging

Soft Skills

strong communication skillscollaborationleadershipproblem-solvingdocumentationstakeholder managementtradeoff managementcode reviewdesign reviewmentoring