Machine Learning Engineer

Career Category

Engineering

Job Description

Role Description 

We are seeking an experienced Machine Learning Engineer to design, develop, deploy, and support scalable machine learning solutions. This role will develop predictive models, build reusable machine learning pipelines, operationalize models through MLOps practices, and monitor model performance in production. 

The ideal candidate has strong hands-on experience with machine learning algorithms, predictive modeling, forecasting, Python, feature engineering, model training and evaluation, AWS cloud services, and production ML operations. Experience developing Generative AI and Large Language Model applications is also preferred. 

The candidate will collaborate with data scientists, data engineers, software engineers, product teams, and business stakeholders to deliver secure, reliable, and scalable machine learning solutions. 

Roles and Responsibilities 

• Design, develop, train, evaluate, and deploy predictive machine learning models for time-series forecasting, classification, regression, anomaly detection, clustering, recommendation, and other business use cases. 

• Perform data exploration, preprocessing, feature engineering, feature selection, and model experimentation. 

• Select appropriate machine learning algorithms, forecasting methods, and evaluation metrics based on business and technical requirements. 

• Build reusable machine learning pipelines covering data ingestion, feature engineering, training, validation, deployment, monitoring, and retraining. 

• Develop forecasting solutions using historical data, time-series features, backtesting, and appropriate validation techniques. 

• Optimize model performance through hyperparameter tuning, cross-validation, experimentation, and error analysis. 

• Develop and maintain production APIs and services that expose machine learning capabilities to applications and downstream consumers. 

• Implement MLOps practices, including experiment tracking, model versioning, model registries, automated testing, CI/CD, and reproducible deployments. 

• Develop, deploy, and operate machine learning workloads primarily on AWS. 

• Develop monitoring and alerting solutions for model accuracy, forecast performance, data quality, drift, bias, latency, reliability, and infrastructure performance. 

• Establish automated or controlled model-retraining and deployment processes. 

• Conduct A/B testing and experimentation to evaluate model and application effectiveness. 

• Develop machine learning solutions that are scalable, secure, explainable, maintainable, and cost-efficient. 

• Implement responsible AI, security, privacy, access-control, and governance requirements. 

• Troubleshoot model, data, pipeline, application, and production-environment issues. 

• Develop Generative AI applications using Large Language Models and Retrieval-Augmented Generation where appropriate. 

• Build LLM solutions involving document processing, chunking, embeddings, vector search, prompt engineering, evaluation, and monitoring. 

• Collaborate with data scientists, data engineers, software engineers, DevOps teams, product teams, and business stakeholders. 

• Participate in technical design discussions, code reviews, sprint planning, backlog refinement, and estimation activities. 

• Maintain model documentation, technical specifications, operational procedures, and deployment standards. 

• Stay current with advances in machine learning, forecasting, MLOps, Generative AI, and cloud technologies. 

• Participate in production support activities, including occasional off-hours support. 

Functional Skills 

Must-Have Skills 

• Strong foundation in supervised and unsupervised machine learning algorithms, predictive modeling, statistical methods, and model evaluation. 

• Strong hands-on experience with Python and SQL. 

• Experience with machine learning libraries such as Scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent technologies. 

• Experience with data preprocessing, feature engineering, model selection, model training, hyperparameter tuning, and evaluation. 

• Hands-on experience developing predictive models and time-series forecasting solutions, including feature engineering, backtesting, model evaluation, and performance monitoring. 

• Experience developing and deploying production machine learning models. 

• Understanding of classification, regression, forecasting, clustering, anomaly detection, and recommendation techniques. 

• Experience implementing MLOps pipelines for model development, deployment, monitoring, versioning, and retraining. 

• Experience with experiment tracking, model registries, data versioning, and reproducible machine learning workflows. 

• Hands-on experience with cloud-based machine learning development and deployment, with AWS strongly preferred. 

• Experience with AWS services such as SageMaker, S3, Lambda, ECR, ECS, EKS, Step Functions, CloudWatch, or equivalent services. 

• Experience building APIs or services for machine learning models. 

• Experience with Git, CI/CD, automated testing, Docker, and software-engineering best practices. 

• Understanding of model monitoring, data drift, concept drift, bias, explainability, and model-performance degradation. 

• Strong analytical, debugging, troubleshooting, and problem-solving skills. 

• Ability to work effectively in Agile and cross-functional delivery teams. 

Good-to-Have Skills 

• Experience with advanced time-series forecasting, natural language processing, computer vision, recommendation systems, or optimization models. 

• Experience with statistical forecasting methods and machine learning-based forecasting approaches. 

• Experience with Amazon SageMaker, MLflow, Databricks Machine Learning, Azure Machine Learning, Vertex AI, or equivalent platforms. 

• Experience with AWS-native MLOps architectures and services. 

• Experience with Kubernetes, infrastructure as code, and cloud-native deployment patterns. 

• Experience with feature stores, distributed model training, automated retraining, and model-serving platforms. 

• Experience with data engineering, ETL/ELT pipelines, Apache Spark, PySpark, or Databricks. 

• Experience with responsible AI, explainability, fairness, model-risk management, and AI governance. 

• Experience building applications using Large Language Models such as OpenAI GPT, Claude, Gemini, Amazon Bedrock models, or equivalent enterprise-approved models. 

• Experience with Retrieval-Augmented Generation, embeddings, vector databases, document processing, prompt engineering, and LLM evaluation. 

• Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or comparable AI application frameworks. 

• Experience with relational, NoSQL, analytical, or vector databases. 

• Experience with Azure or Google Cloud machine learning services is beneficial. 

• Experience delivering solutions within biotechnology, pharmaceutical, life sciences, manufacturing, or another regulated industry. 

Basic Qualifications 

• Master’s or Bachelor’s degree and 5–8 years of experience in Software Engineering, Data Science, Machine Learning Engineering, Computer Science, Information Technology, or a related field. 

Preferred Certifications 

• AWS Certified Machine Learning Engineer – Associate or another relevant AWS certification. 

• AWS Certified Solutions Architect certification. 

• Databricks Machine Learning certification. 

• Microsoft Azure AI Engineer, Azure Data Scientist, or Google Cloud Professional Machine Learning Engineer certification. 

• Relevant DevOps, Kubernetes, data engineering, or AI certification. 

Soft Skills 

• Excellent analytical and troubleshooting skills. 

• Strong verbal and written communication skills. 

• Ability to work effectively with global and virtual teams. 

• High degree of initiative, ownership, and self-motivation. 

• Ability to manage multiple priorities successfully. 

• Team-oriented mindset with a focus on achieving shared goals. 

• Strong presentation and public-speaking skills. 

• Ability to explain machine learning models, forecasts, and results to technical and non-technical stakeholders. 

• Strong attention to detail and commitment to quality and responsible AI practices. 

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