ML Engineer AI DEVELOPMENT: APPLIED MACHINE LEARNING

ABOUT THE ROLE

Tight Line LLC is a software consultancy that takes on complex technical problems for enterprise clients. We are looking for a contract machine learning engineer to work on short-term client projects, typically lasting from a few weeks to several months, across multiple enterprise engagements.

This role focuses primarily on traditional and applied machine learning rather than exclusively on generative AI. Projects may involve computer vision, forecasting and predictive modeling, classification, anomaly detection, recommendation systems, and optimization problems.

You will contribute across the complete machine learning lifecycle: understanding the business problem, preparing data, selecting and training models, evaluating performance, deploying models, monitoring them in production, and improving them over time.

The work is primarily Python-based, using established machine learning, data science, and optimization tools. Experience working with Java-based systems is an advantage, particularly when integrating models into existing enterprise platforms.

You will often be embedded directly with client teams, so this is as much a consulting role as an engineering role. You should be comfortable joining projects already in progress, understanding unfamiliar business domains, communicating tradeoffs clearly, and leaving the client’s team better equipped to maintain the solution after your engagement ends.

WHAT YOU’LL DO

• Design and implement machine learning solutions for real-world business problems

• Develop computer vision systems for tasks such as classification, detection, segmentation, recognition, and visual inspection

• Build forecasting and predictive models using historical, transactional, operational, sensor, or image data

• Formulate and solve optimization problems involving scheduling, allocation, routing, planning, pricing, or resource utilization

• Prepare and validate datasets, including data cleaning, feature engineering, labeling, augmentation, and quality analysis

• Train, tune, compare, and evaluate machine learning models using appropriate metrics and validation strategies

• Develop reproducible training, evaluation, and inference pipelines

• Package and deploy models as APIs, batch processes, scheduled jobs, or components of larger enterprise systems

• Monitor model performance, data quality, drift, latency, and operational reliability in production

• Integrate machine learning solutions with databases, APIs, cloud services, and existing client applications

• Collaborate with software engineers, data engineers, domain experts, and business stakeholders

• Explain modeling decisions, assumptions, limitations, and results to both technical and non-technical audiences

• Pair with client engineers and transfer knowledge so the client can maintain and extend the solution

• Participate in client meetings, technical discussions, demonstrations, and project status updates

WHAT WE’RE LOOKING FOR

• Strong Python engineering and data science skills

• Hands-on experience with common machine learning libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or similar tools

• Experience developing machine learning solutions in at least one of the following areas:

  • Computer vision

  • Forecasting and predictive modeling

  • Classification or anomaly detection

  • Recommendation systems

  • Mathematical or combinatorial optimization

• Experience across the complete machine learning lifecycle, including data preparation, experimentation, model training, evaluation, deployment, monitoring, and maintenance

• Strong understanding of supervised and unsupervised learning, model selection, feature engineering, regularization, validation, and hyperparameter tuning

• Ability to select meaningful evaluation metrics and design experiments that reflect actual business objectives

• Experience identifying and preventing issues such as data leakage, overfitting, sampling bias, class imbalance, and distribution shift

• Experience building maintainable training and inference code rather than working exclusively in exploratory notebooks

• Familiarity with model serving approaches such as REST APIs, batch inference, event-driven processing, or scheduled pipelines

• Testing discipline, including unit, integration, data-quality, and model-performance tests

• Experience with relational databases, data processing pipelines, and common data formats

• Comfortable using Docker and Docker Compose for local development and deployment

• Experience with at least one cloud provider and services such as object storage, managed databases, container deployment, and scheduled workloads

• Ability to work with imperfect, incomplete, and noisy real-world datasets

• Solid software engineering fundamentals, including version control, code review, modular design, documentation, and maintainable code

• A consulting mindset: you listen before prescribing a solution, scope work realistically, communicate risks and tradeoffs honestly, and adapt to unfamiliar domains

• Professional-level English, written and spoken. You will communicate directly with clients, including engineers, managers, and non-technical stakeholders

• Working hours that overlap substantially with US Central, US Mountain, or UK business hours

NICE TO HAVE

• Experience with Java or integrating machine learning models into Java-based enterprise systems

• Experience with computer vision libraries and frameworks such as OpenCV, torchvision, YOLO, Detectron2, MMDetection, or similar tools

• Experience with time-series forecasting tools such as statsmodels, Prophet, Darts, GluonTS, or similar frameworks

• Experience with optimization tools such as Google OR-Tools, Pyomo, CVXPY, Gurobi, CPLEX, or similar solvers

• Experience with MLOps platforms and experiment-tracking tools such as MLflow, Weights & Biases, SageMaker, Vertex AI, or Azure Machine Learning

• Experience with workflow orchestration tools such as Airflow, Prefect, Dagster, or similar platforms

• Experience deploying machine learning systems using Kubernetes

• Experience with model monitoring, drift detection, feature stores, model registries, and automated retraining pipelines

• Experience processing large datasets using Spark, distributed computing, or cloud-native data platforms

• Experience with edge deployment, embedded systems, sensor data, or real-time inference

• Knowledge of natural language processing and generative AI

• Experience integrating large language models, retrieval-augmented generation, or AI agents into broader machine learning systems

• Prior consulting or client-services experience

• Experience translating business requirements into measurable machine learning objectives

DETAILS

• Engagement: Contract and project-based, with the potential for ongoing work across multiple client engagements

• Location: Work from anywhere, provided your working hours substantially overlap one of the time zones listed above

• Travel: Occasional travel to client sites may be required

To apply, send a resume or LinkedIn profile along with a short description of a machine learning system you have built. Explain the business problem, your role, the modeling approach, how the system was deployed, and what made the project technically challenging.