Senior Engineer – AI Full Time NEW

About Manhattan

Manhattan Associates designs, builds, and delivers market-leading supply chain and commerce solutions for its customers around the world. Headquartered in Atlanta, GA, Manhattan Associates entered the software industry in 1990 with one location. Across industries and around the world, leading companies choose Manhattan for supply chain, omnichannel and inventory solutions. We have nearly 4,400 associates located in 16 offices and 13 countries, supported by partners on nearly every continent.

Manhattan Associates continues to be at the forefront of the most innovative technologies. As such, we promote a culture that encourages open minds, fosters superior communication, encourages development, and creates new opportunities for our associates to Push Possible™

JOB SUMMARY

The Sr. Engineer – AI is responsible for end-to-end AI projects from problem definition to model to deployment and monitoring. The primary focus of this role is to combine expertise in machine learning, data engineering, and system architecture to deliver complex AI solutions. This position requires a deep understanding of AI principles, strong problem-solving skills, and the ability to effectively communicate with both technical and non-technical stakeholders.

Must Haves

• 3 to 8 years of experience in AI/ML development, with a proven track record of production-level deployments.
• Expert in Python and familiar with core ML/DL frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers)
• Design and implement integrations using Model Context Protocol to connect AI models with external tools, APIs, and data sources.
• Strong experience developing AI-driven workflows using eknowledge platforms, and AI agent frameworks such as Microsoft Copilot, and Google Agentspace
• Solid understanding of ML theory, statistics, linear algebra, optimization, and deep learning architectures.
• Experience with cloud services (AWS, GCP, Azure), containerization (Docker, Kubernetes), and MLOps tools (MLflow, DVC, Airflow).
• Familiarity with tools for model explainability (SHAP, LIME), monitoring, and continuous retraining.
• Possesses and applies simple to moderate knowledge of particular product or platform to the completion of assignments
• Good communication skills and ability to communicate at some levels of the organization (technical and business)

Good to Haves

• 3+ years experience using Jira, Bitbucket and Confluence agile toolsets or similar
• High proficiency in Microsoft Office suite products
• Good communication skills and ability to communicate at some levels of the organization (technical and business)
• Experience with IT ticketing software (Quality Center, ServiceNow, JIRA)
• Experience using Jira, Bitbucket and Confluence agile toolsets or similar
• Experience working with small, geographically distributed teams
• Experience working both independently and in a team oriented, collaborative environment
• Experience in agile/waterfall software delivery methodologies
• Ability to be flexible while delivering assignments with understanding that deliverables may change based on business needs

EDUCATION REQUIREMENTS

• B.E/B.Tech in Computer Science engineering or related field or equivalent work experience

Principal Duties and Responsibilities

• Problem & solution design

Work with stakeholders to frame business problems as AI/ML use cases

Define success metrics, constraints, and data requirements

• Data engineering & preparation

Ingest, clean, and transform data from multiple sources

Build and maintain feature pipelines / datasets for training and inference

• Model development

Select appropriate algorithms (ML, deep learning, NLP, recommendation, etc.)

Train, tune, and validate models using best practices (cross‑validation, baselines, ablation, etc.)

• Evaluation & experimentation

Design experiments (A/B tests, offline metrics) to compare models and approaches

Analyze results and iterate quickly to improve performance and robustness

• MLOps & deployment

Package and deploy models to production (APIs, batch jobs, streaming)

Implement CI/CD for ML, versioning of data/models, and reproducible training

• Monitoring & maintenance

Set up monitoring for drift, performance, latency, cost, and failures

Retrain, recalibrate, or roll back models as data and business conditions change

Keeps abreast of improvements in software techniques and develop some improvements on own