Enablement Engineer

NTT DATA; Atlanta (GA); Presencial; USD 96.000 - 110.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical insurance

Dental insurance

Vision insurance

HSAs/FSAs

401k plan

Employee assistance

Descripción de la vacante

NTT DATA is seeking an AI Enablement Engineer to join its client team in Charlotte, NC. You will design, deploy, and scale enterprise ML and GenAI capabilities, partnering with CIO-aligned teams to turn business priorities into production-grade AI solutions.

You will work across data prep, model development, secure GenAI design, ML Ops, and API exposure, with a focus on responsible AI, governance, and observability in a large enterprise environment.

Formación

• 3+ years in software/data/ML/AI in enterprise environments.
• 3+ years Python with NumPy, Pandas, scikit-learn, Matplotlib, Seaborn.
• 3+ years building ML models with supervision and/or unsupervised methods.
• 2+ years DL work with PyTorch or TensorFlow/Keras.
• 2+ years GenAI/LLM applications including prompt engineering, RAG, embeddings.
• 2+ years AI workflows with orchestration, APIs, and enterprise integrations.
• 3+ years SQL, data modeling, ETL/ELT, data prep.
• 3+ years cloud AI/ML services on Azure/AWS/Google Cloud.
• 5+ years API/backend dev with Git, CI/CD, Docker; Kubernetes preferred.
• 2+ years MLOps/LLMOps with MLflow or similar.
• Strong foundations in linear algebra, stats, calculus, optimization.
• Proven problem-solving, communication, leadership.

Responsabilidades

• Lead AI/ML engineering within CIO organization, translating business problems into measurable ML use cases.
• Design, develop, test, deploy, and support production-grade ML and GenAI solutions.
• Prepare and analyze data using Python, NumPy, Pandas, Matplotlib, Seaborn.
• Develop ML models with diverse algorithms including regression, trees, boosting, clustering.
• Build DL solutions with PyTorch or TensorFlow; apply transformers and sequence models.
• Design secure GenAI solutions with prompt engineering, embeddings, and RAG.
• Establish experiments with cross-validation and relevant metrics.
• Build SQL data pipelines; use Spark/Databricks for large-scale processing.
• Expose models via secure REST APIs (FastAPI/Flask).
• Implement MLOps/LLMOps: tracking, versioning, CI/CD, testing, monitoring.
• Drive adoption of Azure AI, Azure OpenAI, AWS Bedrock; create reusable components.
• Ensure compliance with architecture, security, privacy, responsible AI, governance.
• Lead technical workshops and knowledge-sharing sessions.

Conocimientos

Python

NumPy

Pandas

scikit-learn

Matplotlib

Seaborn

ML & AI

PyTorch

TensorFlow

SQL

ETL/ELT

REST APIs

MLOps/LLMOps

CI/CD

Docker

Kubernetes

MLflow

Educación

Bachelor's degree in Computer Science, Engineering, Data Science, AI, ML, Math, Stats, or related field

Herramientas

Spark

Databricks

Kubernetes

MLflow

LangChain

Azure OpenAI/AWS Bedrock

Pinecone/Weaviate/Chroma

Descripción del empleo

Company Overview

Req ID: 397017

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, NTT DATA's Client is currently seeking an AI Enablement Engineer to join their team in Charlotte, North Carolina (US-NC), United States (US).

Location: U.S. Bank hubs (Chicago, Charlotte, Atlanta, Cincinnati, Minneapolis, Northern California, and Irving, TX)

Role Summary

The AI/ML Engineer / AI Enablement Engineer will design, build, deploy, and scale enterprise machine learning and generative AI solutions. This role combines hands-on engineering with technical enablement, partnering with CIO-aligned teams to translate business priorities into secure, responsible, observable, and production-ready AI capabilities.

Day-to-Day Responsibilities

• Partner with an assigned CIO organization as a primary AI/ML engineering lead, converting business problems into measurable machine learning and generative AI use cases.
• Design, develop, test, deploy, and support production-grade solutions spanning predictive machine learning, deep learning, large language models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI.
• Prepare and analyze structured and unstructured data using Python, NumPy, Pandas, Matplotlib, and Seaborn; perform data quality analysis, preprocessing, feature engineering, and exploratory analysis.
• Develop supervised and unsupervised learning solutions using techniques such as linear and logistic regression, decision trees, random forests, gradient boosting/XGBoost, support vector machines, K-Means, DBSCAN, PCA, and other dimensionality-reduction methods.
• Build deep learning solutions with PyTorch (preferred) or TensorFlow/Keras, applying neural networks, CNNs, RNNs/LSTMs, transformers, attention mechanisms, and transfer learning where appropriate.
• Design secure GenAI solutions using prompt engineering, embeddings, vector search, document ingestion, RAG, fine-tuning, tool/function calling, orchestration frameworks, AI agents, evaluation, and guardrails.
• Establish statistically sound experiments and evaluate solutions using cross-validation and relevant measures such as accuracy, precision, recall, F1 score, ROC-AUC, calibration, latency, cost, groundedness, relevance, and safety.
• Build and maintain SQL-based data pipelines, ETL/ELT workflows, data models, and integrations with enterprise data sources; use Spark or Databricks for large-scale processing when needed.
• Expose models and AI capabilities through secure REST APIs and services using FastAPI, Flask, or comparable frameworks, and integrate them with enterprise applications and backend systems.
• Implement MLOps/LLMOps practices, including experiment tracking, model and prompt versioning, CI/CD, automated testing, containerization, orchestration, model serving, monitoring, drift detection, observability, and controlled releases.
• Drive adoption of Azure AI, Azure OpenAI, AWS Bedrock, and internal AI platforms; create reusable components, reference architectures, sample code, playbooks, and implementation patterns.
• Ensure solutions comply with enterprise architecture, cybersecurity, privacy, responsible AI, model risk management, data governance, accessibility, resilience, and production support standards.
• Lead technical workshops, design reviews, mentoring, and knowledge-sharing sessions for engineering, product, data, risk, and business stakeholders.

Basic Qualifications

• 3 years of overall experience in software engineering, data engineering, machine learning engineering, data science, or AI solution development in enterprise environments.
• 3 years of hands-on Python development using libraries such as NumPy, Pandas, scikit-learn, Matplotlib, and Seaborn, along with object-oriented programming, data structures and algorithms, design patterns, unit testing, and maintainable coding practices.
• 3 years of experience developing and evaluating supervised and unsupervised machine learning models, including feature engineering, hyperparameter tuning, cross-validation, and model performance analysis.
• 2 years of experience developing deep learning solutions with PyTorch or TensorFlow/Keras and working knowledge of neural networks, transformers, attention, CNNs, and sequence models.
• 2 years of hands-on experience building GenAI/LLM applications, including prompt engineering, RAG, embeddings, vector databases/search, model integration, evaluation, guardrails, and production deployment.
• 2 years of experience developing agentic or multi-step AI workflows using orchestration frameworks, tool/function calling, APIs, and enterprise system integrations.
• 3 years of experience with SQL, data modeling, data preprocessing, ETL/ELT pipelines, feature engineering, and relational or NoSQL databases.
• 3 years of experience with cloud AI/ML services on Microsoft Azure, AWS, or Google Cloud, including secure deployment, identity, networking, storage, and monitoring patterns.
• 5 years of experience building and deploying APIs or backend services and using Git, CI/CD, Docker, and cloud-native production practices; Kubernetes experience is strongly preferred.
• 2 years of experience with MLOps/LLMOps, including MLflow or comparable tooling, experiment tracking, model registries, versioning, automated testing, serving, and production monitoring.
• Applied understanding of linear algebra, probability, statistics, calculus, optimization, hypothesis testing, confidence intervals, correlation, and covariance as used in model development and experimentation.
• Demonstrated problem-solving, communication, stakeholder management, and technical leadership skills, with the ability to explain complex AI/ML concepts to technical and non-technical audiences.

Core Technology Stack

• Programming and analytics: Python, NumPy, Pandas, scikit-learn, Matplotlib, Seaborn, SQL.
• Machine learning and deep learning: XGBoost, PyTorch, TensorFlow/Keras, MLflow.
• GenAI and orchestration: LangChain/LangGraph, LlamaIndex, Semantic Kernel, OpenAI/Azure OpenAI APIs, AWS Bedrock, or comparable frameworks and services.
• Search and vector technologies: Azure AI Search, Pinecone, Weaviate, Chroma, OpenSearch, pgvector, FAISS, or equivalent platforms.
• Data platforms and databases: SQL Server, PostgreSQL, MySQL, MongoDB, Spark, Databricks, and vector databases.
• Deployment and collaboration: REST APIs, FastAPI/Flask, Docker, Kubernetes, Git, GitHub, Azure DevOps, CI/CD, Agile/Scrum.

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related field, or equivalent practical experience.

Travel

Travel as required based on business and CIO-aligned engagement needs.

Preferred Qualifications

• Experience delivering AI/ML solutions in financial services or another highly regulated environment, including familiarity with model risk management and responsible AI controls.
• Experience building reusable AI accelerators, reference architectures, shared platform capabilities, or internal developer enablement programs.
• Experience with distributed data processing and big-data ecosystems, particularly Spark, Databricks, or Hadoop technologies.
• Experience with advanced LLM techniques such as fine-tuning, synthetic data generation, multimodal models, agent evaluation, red-teaming, and human-in-the-loop workflows.
• Master's degree or higher in a relevant technical or quantitative discipline.

About NTT DATA

NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long-term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation, and management of applications, infrastructure, and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com

Compensation and Benefits

U.S. On Location / Hybrid NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this role is $70/Hr to $80/Hr. Actual compensation will depend on a number of factors, including the candidate’s relevant experience, technical skills, and other qualifications. This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD&D insurance, employee assistance, participation in a 401k program, and additional voluntary or legally-required benefits.

Accessibility and Equal Opportunity

NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement. If you'd like more information on your EEO rights under the law. For Pay Transparency information.