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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI / ML Engineer - **Company:** LTM Inc - **Location:** Raritan, NJ, United States - **Experience:** Experienced - **Salary:** $70,000.0 - $100,000.0 - **Contract:** Internship / Graduate position - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Computer Vision, Automated Storage and Retrieval Systems, Computer Programming, Continuous Integration, Data Cleansing, Information Engineering, Data Files, Data Infrastructure, Data Warehousing, Relational Databases, Monitoring of Systems, JSON, Python (Programming Language), Machine Learning, NumPy, Cloud Services, Tensorflow, Standard Sql, SciPy, Search Technologies, Software Engineering, Web Applications, Extensible Markup Language (XML), Data Logging, Cloud Platform System, Feature Engineering, Pytorch, Large Language Models, Snowflake, Random Forest, Deep Learning, Model Validation, Generative AI, AWS Lambda, Pandas, Matplotlib, Git Flow, Scikit Learn, Information Technology, HuggingFace, Data Analytics, Star Schema, Machine Learning Operations, Feature Extraction, Document Classification, Artificial Intelligence Markup Language (AIML), Software Version Control, Data Pipelines, Docker - **Published:** July 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ca3a80ab86734aea ## About the Role We are looking for an AI / ML Engineer with strong software engineering fundamentals and handson experience building productiongrade Python systems scalable data pipelines and machine learning solutions in cloud environments The ideal candidate will have practical experience across the machine learning lifecycle including data preparation feature engineering model development evaluation deployment support monitoring and documentation This role is well suited for an engineer who can work at the intersection of machine learning data engineering and cloudbased software development The candidate should be comfortable developing MLdriven automation solutions working with structured and semistructured data collaborating with crossfunctional teams and translating research or prototype ideas into reliable engineering solutions, 4 years of professional experience in software engineering data engineering machine learning engineering or related technical roles Strong programming experience in Python with working knowledge of SQL and familiarity with R or C as an added advantage Handson experience with scientific Python and ML libraries such as NumPy Pandas Matplotlib scikitlearn SciPy PyTorch TensorFlow HuggingFace and Transformerbased models Experience developing machine learning models using algorithms such as Random Forest ensemble methods deep learning models NLP models computer vision models and embeddingbased retrieval systems Strong understanding of data preprocessing feature engineering model evaluation metrics class imbalance handling validation techniques and statistical testing Experience designing and maintaining scalable ETLELT pipelines and data workflows using Snowflake AWS and Python Working knowledge of cloud services especially AWS services such as S3 Lambda Glue and cloudnative data infrastructure Experience with data quality monitoring schema management anomaly detection logging and pipeline reliability practices Familiarity with Docker CICD pipelines Gitbased collaboration technical documentation and production software development practices Ability to communicate technical concepts effectively to both technical and nontechnical stakeholders Preferred GoodtoHave Skills Experience with MLOps concepts such as model versioning experiment tracking model deployment model monitoring and automated retraining workflows Experience with multimodal AI imagetext embeddings semantic search contentbased retrieval or vector similarity search Handson experience with computer vision use cases including CNNbased classification image feature extraction and dataset quality analysis Experience with NLP use cases including BERT Transformer finetuning speech or text classification and emotion or intent detection Exposure to largescale datasets data warehousing star schema design outlier detection and analyticsready data modeling Research experience publication experience or demonstrated ability to convert research concepts into applied ML solutions AWS certification or equivalent cloud certification Experience building lightweight web applications or APIs for ML model serving such as Flaskbased applications Education and Certifications Bachelors or Masters degree in Computer Science Data Analytics Information Technology Artificial Intelligence Machine Learning Statistics or a related field Advanced academic background in Computer Science or Data A Skills Mandatory Skills : AI/ML Testing, GenAI - LLMOps, Generative AI/Open AI/Vector DB, Industrial AI - Machine Learning (ML), Python Good to Have Skills : AI/ML Awareness Testing, AI_Implementation_Infra - Vector Store DB, RAG, Apache Airflow, AWS Lambda, LangChain, MLOPS, MLOPS - Python, Vertex AI Other details ## Description Design develop and maintain machine learning models and AIdriven systems using Python and modern ML libraries Build and optimize data pipelines for heterogeneous data sources such as CSV JSON XML and relational databases Perform data preprocessing feature engineering exploratory data analysis model training validation and performance evaluation Develop ML solutions for use cases involving classification forecasting embeddings NLP computer vision similarity search and intelligent automation Implement scalable ETLELT workflows using Python Snowflake and cloud services such as AWS S3 Lambda and Glue Support deploymentready ML workflows including model monitoring data quality checks logging error handling and ing Collaborate with data scientists software engineers product teams and business stakeholders to understand requirements and deliver practical AIML solutions Conduct experiments compare model architectures tune hyperparameters analyze model performance and document findings clearly Develop reusable maintainable and welltested code following software engineering best practices Git workflows and CICD standards Stay current with advances in machine learning deep learning NLP computer vision embeddings and cloudbased AIML platforms ## Related Videos - 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