Sr. AI-ML Engineer Data Analytics TK Elevator Corporation

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TK Elevator is currently seeking a highly skilled and innovative Sr. AI-ML Engineer Data Analytics located in Atlanta, GA.

Responsiblefor designing, building, and operationalizing machine learning and AI solutions across the full model lifecycle—from data preparation and feature engineering through deployment and production monitoring. The Sr. AI/ML Engineer must combine deep hands-on ML expertise with strong engineering discipline to translate complex business problems into reliable, scalable AI systems that deliver measurable value across the manufacturing enterprise.

ESSENTIAL JOB FUCTIONS: Design and implement end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring. Lead AI/ML use-case development—problem framing, data exploration, algorithm selection, experimentation, and productionization for manufacturing and enterprise applications. Apply supervised, unsupervised, and generative AI techniques including NLP, computer vision, time-series forecasting, and LLM-based applications to business use cases. Design and maintain MLOps infrastructure including model registries, CI/CD pipelines, experiment tracking, and deployment automation on cloud platforms (Azure, AWS, or GCP). Establish model monitoring, drift detection, alerting, and retraining triggers to maintain accuracy and reliability in production. Build data preparation and transformation pipelines for ML workflows, ensuring quality, consistency, and reproducibility across training and inference environments. Partner with the Data Governance Office and business data owners to ensure training datasets comply with lineage, privacy, and regulatory requirements. Provide technical guidance and peer review across AI/ML initiatives; mentor junior engineers and data scientists on engineering best practices and production-readiness. Collaborate with business and IT stakeholders to translate use case requirements into technical specifications and communicate model results in accessible terms. Evaluate emerging AI/ML frameworks and tools; recommend adoption paths that advance the team's modeling and operational maturity. EDUCATION & EXPERIENCE: Bachelor's degree in Computer Science, Data Science, Statistics, Electrical Engineering, or a related field; Master's or PhD preferred. Or equivalent work experience. 6+ years of hands-on ML engineering or data science experience, with at least 2 years focused on production ML systems in enterprise environments. Proven expertise deploying models on cloud ML platforms (Azure Machine Learning, Databricks ML, AWS SageMaker, or equivalent); proficiency in Python and key ML libraries (scikit-learn, PyTorch, TensorFlow, XGBoost, or similar). Strong MLOps experience: experiment tracking, model registries, automated retraining, containerization (Docker, Kubernetes), and CI/CD for ML. Experience with feature engineering, data pipeline development (Spark, dbt, Azure Data Factory, Databricks), and responsible AI principles including explainability and bias mitigation. Familiarity with data privacy and compliance frameworks (GDPR, SOX) as they apply to ML training data and model outputs. Strong communication skills; ability to explain model behavior and trade-offs to both technical and non-technical audiences. ACCOUNTABILITES: Perform functional and non-functional requirements analysis for ML use cases and translate findings into solution designs. Maintain high availability and reliability of deployed models and ML pipelines through defined monitoring and operational standards. Timely resolution of model performance issues, pipeline failures, and production incidents. Accurate and timely delivery of assigned ML development components within program plans. Experience with LLMs, prompt engineering, retrieval-augmented generation (RAG), and generative AI frameworks (LangChain, Azure OpenAI, or equivalent), preferred. Experience applying ML to manufacturing or IoT use cases: predictive maintenance, demand forecasting, quality control, or operational optimization, preferred. AI/ML certifications: Azure AI Engineer Associate, Databricks Certified ML Professional, AWS ML Specialty, or Google Professional ML Engineer, preferred.

Provided they meet all eligibility requirement under the applicable plan documents, employees will be offered

Medical, dental, and vision coverage Flexible spending accounts (FSA) Health savings account (HSA) Supplemental medical plans Company-paid short- and long-term disability insurance Company-paid basic life insurance and AD&D Optional life and AD&D coverage Optional spouse and dependent life insurance Identity theft monitoring Pet insurance Company-paid Employee Assistance Program (EAP) Tuition reimbursement 401(k) Retirement Savings Plan with company match: Employees can contribute a portion of their pay on a pre-tax or Roth basis. The company provides a dollar-for-dollar match on the fir