Business Consultant - AI/ML Engineer

NTT DATA Services•Atlanta, GA

18h•$91,000 - $130,000•Hybrid

About The Position

We are seeking an experienced Business Consultant – AI/ML Engineer to design, develop, and deploy scalable Artificial Intelligence and Machine Learning solutions. The ideal candidate should have strong hands-on experience with Python, machine learning frameworks, data processing, and cloud-based AI/ML services. The role will collaborate with data scientists, data engineers, application developers, and business stakeholders to build production-ready ML solutions and integrate AI capabilities into enterprise applications.

Requirements

5+ years of experience in Python development, Data Science, or Machine Learning engineering.
3+ years of experience developing ML solutions using Scikit-learn, TensorFlow, PyTorch, or similar frameworks.
3+ years of experience with data processing, feature engineering, SQL, and model development.
2+ years of experience deploying AI/ML solutions on AWS, Azure, or Google Cloud Platform.

Nice To Haves

Experience with Generative AI, Large Language Models (LLMs), and Agentic AI.
Knowledge of RAG, embeddings, vector databases, and semantic search.
Experience with LLM frameworks such as LangChain, LlamaIndex, or similar technologies.
Experience with Azure OpenAI, Amazon Bedrock, Vertex AI, or similar AI platforms.
Knowledge of MLOps tools and model lifecycle management.
Experience with Docker, Kubernetes, and CI/CD pipelines.
Familiarity with Spark, Databricks, Snowflake, or other enterprise data platforms.
Understanding of responsible AI, model governance, security, and data privacy.
Experience developing AI solutions within Banking, Financial Services, Healthcare, Retail, or other enterprise domains.
Strong analytical, problem-solving, communication, and stakeholder management skills.

Responsibilities

Design, develop, train, and deploy Machine Learning models for enterprise use cases.
Develop data preprocessing, feature engineering, model training, and evaluation pipelines.
Build and integrate AI/ML services with enterprise applications using APIs and Microservices.
Evaluate ML algorithms and optimize models for accuracy, performance, and scalability.
Develop automated ML pipelines for model training, testing, deployment, and monitoring.
Collaborate with Data Scientists, Data Engineers, and business teams to translate requirements into AI/ML solutions.
Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management.
Work with structured and unstructured datasets from multiple sources.
Monitor production models and address model performance, drift, and data-quality issues.
Participate in code reviews, technical design discussions, and Agile delivery activities.