We are seeking a highly skilled and experienced Senior Machine Learning Engineer to drive the development, deployment, and monitoring of cutting-edge machine learning solutions in a scalable cloud environment. You will collaborate with a cross-functional team to deliver end-to-end ML pipelines, ensure high-quality model performance, and implement robust monitoring for production systems.
responsibilities
Reproduce training environments with pinned dependencies and seeds, rebuild feature and label pipelines, and register models
Deploy SageMaker endpoints and batch jobs with autoscaling, execute shadow/canary strategies, and define rollback protocols
Set up ML experiments, Model Registry, and Feature Stores, integrating them into CI/CD workflows
Implement Model Monitor for drift and performance tracking, configure alerting mechanisms, and create comprehensive runbooks
Optimize pipelines for reliable model serving, ensuring low latency, high quality, and rollback readiness during hypercare
Collaborate with data engineers to build scalable data pipelines on S3/Snowflake for training and inference workflows
requirements
3+ years of hands-on experience in machine learning engineering, with a solid background in Python 3.x
Proficiency in PyTorch or TensorFlow, with a track record of building and deploying models in production
Expertise in AWS SageMaker, including Pipelines, Registry, Endpoints, and Model Monitor
Familiarity with MLflow or SageMaker Experiments for experiment tracking and artifact management
Strong skills in Docker for containerization of ML workloads
Experience with Feature Store solutions such as SageMaker Feature Store or Feast
English level B1+ for effective communication
nice to have
Proficiency in FastAPI or BentoML for lightweight API service development
Knowledge of ONNX for model optimization and cross-platform interoperability
Familiarity with distributed inference tools like Ray Serve or Triton
Experience with large-scale distributed training using Horovod or DeepSpeed
Understanding of Hugging Face Transformers for state-of-the-art NLP model implementation
We are searching for a Lead Machine Learning Engineer who will be responsible for designing ML models that undergo ongoing training and developing frameworks suited for data scientists. If you possess exceptional skills in developing and implementing production applications and data pipelines, consider applying for this position.
responsibilities
Oversee the migration of machine learning algorithms to a production setting and their integration within the company's existing systems
Create, manage, and improve the entire machine learning lifecycle from start to finish
Draft detailed specifications, documentation, and user manuals
Enhance SDLC processes, set up and manage CI/CD/CT frameworks
Implement mechanisms for the early detection of different types of drifts (data, concept, schema, etc.)
requirements
5+ years of enterprise software development experience
3+ years of Machine Learning experience
Qualifications in designing, constructing and launching production applications and data pipelines
Skills in developing highly resilient, scalable applications and systems powered by ML
Hands-on expertise in establishing SDLC best practices
Understanding of Python development
Familiarity with Cloud-native services (GCP, AWS, Azure) and Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML)
nice to have
Expertise in MLOps-related platforms and technologies
Experience in integrating ML models within multifaceted data-driven systems, IoT devices, or mobile devices
Proficiency in basic software engineering tools (CI/CD environments like Jenkins or BuildKit, PyPI, Docker, and Kubernetes)
We are seeking a highly skilled Senior Machine Learning Engineer to join our remote team. As a Senior Machine Learning Engineer, you will be responsible for designing and implementing machine learning solutions for our clients using your extensive experience in Data Science and Machine Learning Engineering. You will be working with large-scale datasets and using your expertise to develop and deploy industrial-scale ML solutions. You will also be responsible for communicating with client stakeholders on both the technical and business side to ensure delivery is client driven. If you have a passion for designing and implementing machine learning solutions, we encourage you to apply.
responsibilities
Design and implement machine learning solutions for clients using your expertise in Data Science and Machine Learning Engineering
Work with large-scale datasets and develop and deploy industrial-scale ML solutions
Communicate with client stakeholders on both the technical and business side to ensure delivery is client driven
Integrate ML solutions into cloud services such as AWS SageMaker, S3, and Lambda
Analyze and interpret data and provide clear and comprehensive reports
Collaborate with cross-functional teams to conceptualize, design, develop, and implement effective machine learning solutions
requirements
3+ years of experience in Data Science and Machine Learning Engineering
Strong understanding of Amazon Web Services (AWS) and experience with AWS SageMaker, S3, Lambda, and other cloud services
Experience in time series forecasting and working with R-based solutions
Experience with orchestration and experiment tracking tools such as AirFlow, MLFlow, and DVC
Ability to communicate effectively with client stakeholders on both the technical and business side
Fluent English language skills with an Upper-Intermediate level as a minimum
nice to have
Experience with Snowflake, R time series libraries, AWS CodePipeline, DataBricks, and k8s
We are seeking a seasoned Senior Machine Learning Engineer to bolster our team. This role is ideal for a professional eager to leverage their AI and machine learning skills to drive transformative projects and solutions. The chosen candidate will play a pivotal role in enhancing and optimizing our advanced machine learning models and systems to meet the diverse demands of our applications.
responsibilities
Design machine learning models to address complex challenges
Collaborate with multidisciplinary teams to understand business needs and identify machine learning opportunities
Implement robust machine learning infrastructures in live environments
Evaluate and analyze data to ensure model accuracy and reliability
Improve the performance and efficiency of existing machine learning models
Guide and support junior engineers while leading machine learning projects
Stay informed about the latest trends and advancements in machine learning technologies
Incorporate new data sources to boost model capabilities
Produce detailed documentation on machine learning techniques and model structures
Share technical insights and model outcomes with stakeholders and team members
requirements
A minimum of 3 years of experience in Machine Learning Engineering, with expertise in AWS technologies
Proficiency in Amazon Bedrock for the creation and deployment of machine learning solutions
Extensive background in using Amazon SageMaker to develop and manage machine learning models
Comprehensive familiarity with Amazon S3 for effective data storage and handling in machine learning projects
Advanced expertise in Python for machine learning model development
In-depth knowledge of Natural Language Processing (NLP) and its practical applications
Demonstrated capability in applying Deep Learning techniques
Competency in Prompt Engineering to enhance model interaction
Skills in implementing Sentiment Analysis for practical use cases
Understanding of effective Model Evaluation for NLP to validate performance
Fluency in English at a B2 level or higher for seamless collaboration and stakeholder communication
nice to have
Understanding of other cloud platforms, including Google Cloud or Microsoft Azure
Proficiency in additional programming languages such as R or Java
Qualifications in machine learning, data science, or related disciplines
We are searching for a skilled Senior Machine Learning Engineer to enhance our team. This position is ideal for professionals keen to apply their AI and machine learning capabilities to lead impactful projects and solutions. The candidate will focus on optimizing and advancing sophisticated machine learning systems to meet diverse application needs.
responsibilities
Design machine learning models to solve complex problems
Collaborate with interdisciplinary teams to understand business needs and identify machine learning opportunities
Deploy robust infrastructures for machine learning in production environments
Analyze data to validate model accuracy and reliability
Improve the performance and capabilities of existing machine learning models
Guide junior engineers and drive machine learning projects
Stay informed about the latest machine learning trends and advancements
Evaluate and integrate new data sources to expand model functionality
Document machine learning workflows and model architectures comprehensively
Present technical insights and model results to stakeholders and colleagues
requirements
Minimum of 3 years of experience in Machine Learning Engineering, emphasizing AWS technologies
Competency in Amazon Bedrock for creating and deploying machine learning solutions
Proficiency in Amazon SageMaker for building and maintaining machine learning workflows
Understanding of Amazon S3 for optimized data storage and processing in ML projects
Expertise in Python for the development of machine learning models
Background in Natural Language Processing (NLP) and real-world applications
Knowledge of Deep Learning strategies and their practical use
Skills in Prompt Engineering to refine model interactions
Capability to implement Sentiment Analysis in operational use cases
Familiarity with Model Evaluation practices for NLP in performance validation
Comfortable using English at a B2 level or higher for effective collaboration and communication
nice to have
Background in alternative cloud solutions, including Google Cloud or Microsoft Azure
Proficiency in supplementary programming languages such as R or Java
Qualifications in machine learning, data science, or relevant technical fields
We are looking for a highly skilled Senior Machine Learning Engineer to design and implement machine learning models and systems that address complex challenges and drive meaningful business outcomes. You will play a critical role in extracting insights from data, building advanced pipelines, and leveraging generative AI to develop impactful solutions. This position combines technical proficiency, innovative thinking, and collaboration to deliver scalable results aligned with organizational goals.
responsibilities
Design and implement machine learning solutions tailored to domain-specific business needs
Leverage data science techniques, classical machine learning methods, and generative AI to extract actionable insights
Build, deploy, and optimize ML pipelines, addressing feature generation, model evaluation, retraining, and automation
Interpret analytical results and model outputs, translating them into clear, actionable strategies for stakeholders
Collaborate with teams across the organization to gather requirements and deliver impactful machine learning solutions
Research and integrate emerging ML and AI technologies to improve processes and systems
Document workflows, algorithms, and findings to support reproducibility and knowledge sharing
requirements
3+ years of hands-on experience applying data science and machine learning techniques to solve real-world problems
Strong ability to define business problems and design ML-driven solutions with an analytical focus
Expertise in Python and SQL, with proficiency in frameworks such as Scikit-learn, Pandas, and PyTorch
Solid understanding of statistical methods, machine learning algorithms, and advanced techniques like deep learning or LLMs (Large Language Models)
Experience applying statistical concepts such as regressions, A/B tests, clustering, and probability to business challenges
Skills in storytelling through data visualization tools like Tableau, R-Shiny, Streamlit, or Looker
Excellent communication skills, with the ability to present technical concepts to both technical and non-technical audiences
Proven problem-solving skills with the ability to independently manage and take ownership of projects
Collaborative mindset with experience working in cross-functional teams to align objectives and deliver results
Excellent command of written and spoken English (B2+ level)
nice to have
Master’s or PhD degree in Computer Science, AI, Machine Learning, or a related field
We invite applications for the position of Senior Machine Learning Engineer , responsible for architecting, implementing, and optimizing advanced machine learning systems to solve complex business challenges.
responsibilities
Stay current with the latest generative AI, general Data Science and machine learning frameworks
Design, develop, and deploy scalable machine learning models and end-to-end pipelines in production environments
Conduct rigorous data preprocessing, feature engineering, and model selection to maximize performance and reliability of ML instruments
Implement MLOps best practices for continuous integration, delivery, and monitoring of machine learning systems
Optimize model performance through hyperparameter tuning, distributed training, and hardware acceleration (e.g., GPU, TPU)
Ensure robust model versioning, reproducibility, and documentation throughout the development lifecycle
Collaborate with data engineers, software developers, and DevOps teams to integrate ML solutions into existing platforms
Maintain compliance with data security, privacy, and governance standards across all ML workflows
requirements
Over 6 years of professional experience in machine learning engineering, data science, or related fields on large-scale projects
Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost
Strong experience with cloud platforms (e.g., Microsoft Azure, AWS, GCP, OCI, IBM Cloud) for ML model development and deployment
Deep understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow, CI/CD pipelines)
Expertise in building and optimizing data pipelines, ETL processes, and model serving architectures
Solid knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes)
Advanced SQL and experience with NoSQL databases for handling structured and unstructured data
Ability to write clean, efficient, and well-documented code, following software engineering best practices
B2 level of English or higher, with strong technical communication skills
nice to have
Experience with deep learning for NLP, computer vision, or time series analysis
Familiarity with distributed computing frameworks (e.g., Spark, Dask, Ray)
Exposure to edge deployment of ML models and hardware-specific optimizations
Contributions to open-source ML projects or relevant scientific publications
We are seeking a Lead Machine Learning Engineer (Personalization & AI Models) to pioneer the building and optimization of user segmentation, recommendation, and embedding models within our expansive personalization system for a Mobile App. The focus areas will include multi-vector representations, real-time model inference, and the integration of personalization workflows with cutting-edge technologies including AWS Personalize SDK, PGVector, and RudderStack.
responsibilities
Develop and optimize embedding models using sentence-transformer models for user profiles and personalization
Implement KNN-based recommendation systems for real-time content scoring and ranking
Utilize AWS Personalize SDK to train and deploy machine learning models that dynamically adapt to user behavior
Integrate embedding models with Databricks, RudderStack, and AWS services to ensure real-time profile updates
Fine-tune ML models aimed at boosting revenue predictions, enhancing user engagement, and refining audience segmentation
Optimize PGVector and Redis for efficient vector-based lookups and caching
Collaborate with data engineers to devise ML pipelines for robust training, validation, and deployment processes
requirements
Strong experience in Machine Learning, Deep Learning, and AI-driven personalization
Proficiency in Python, PyTorch, and TensorFlow
Expertise in vector-based search and recommendation systems, including knowledge of KNN, PGVector, Redis
Hands-on experience with AWS Personalize SDK and SageMaker or similar ML training pipelines
Familiarity with Databricks, Delta Lake, and Apache Spark for large-scale model training and deployment
Strong understanding of real-time personalization, A/B testing, and user segmentation models
Capability to work with event-driven architectures and implement real-time feature engineering
Fluent English communication skills at a B2+ level
nice to have
Knowledge of ML model monitoring, MLOps, and automation of ML pipelines through CI/CD
Experience with Graph Neural Networks (GNNs) for analyzing user similarity and clustering
Familiarity with deployment of real-time analytics tools and dashboarding, such as Looker, Tableau, or Snowflake
We are seeking a Lead Machine Learning Engineer (Personalization & AI Models) to pioneer the building and optimization of user segmentation, recommendation, and embedding models within our expansive personalization system for a Mobile App. The focus areas will include multi-vector representations, real-time model inference, and the integration of personalization workflows with cutting-edge technologies including AWS Personalize SDK, PGVector, and RudderStack.
responsibilities
Develop and optimize embedding models using sentence-transformer models for user profiles and personalization
Implement KNN-based recommendation systems for real-time content scoring and ranking
Utilize AWS Personalize SDK to train and deploy machine learning models that dynamically adapt to user behavior
Integrate embedding models with Databricks, RudderStack, and AWS services to ensure real-time profile updates
Fine-tune ML models aimed at boosting revenue predictions, enhancing user engagement, and refining audience segmentation
Optimize PGVector and Redis for efficient vector-based lookups and caching
Collaborate with data engineers to devise ML pipelines for robust training, validation, and deployment processes
requirements
Strong experience in Machine Learning, Deep Learning, and AI-driven personalization
Proficiency in Python, PyTorch, and TensorFlow
Expertise in vector-based search and recommendation systems, including knowledge of KNN, PGVector, Redis
Hands-on experience with AWS Personalize SDK and SageMaker or similar ML training pipelines
Familiarity with Databricks, Delta Lake, and Apache Spark for large-scale model training and deployment
Strong understanding of real-time personalization, A/B testing, and user segmentation models
Capability to work with event-driven architectures and implement real-time feature engineering
Fluent English communication skills at a B2+ level
nice to have
Knowledge of ML model monitoring, MLOps, and automation of ML pipelines through CI/CD
Experience with Graph Neural Networks (GNNs) for analyzing user similarity and clustering
Familiarity with deployment of real-time analytics tools and dashboarding, such as Looker, Tableau, or Snowflake
We are seeking a skilled Senior Machine Learning Engineer to enhance our team. This role is ideal for a professional with AI and machine learning expertise, ready to drive innovative projects and solutions. The successful candidate will be instrumental in optimizing and advancing our machine learning models and systems to meet diverse application requirements.
responsibilities
Design machine learning models to solve complex problems
Collaborate with cross-functional teams to identify business needs and apply machine learning strategies
Build and manage scalable machine learning infrastructures for production
Analyze datasets to verify model accuracy and reliability
Boost the performance and scalability of existing machine learning models
Guide junior engineers and direct machine learning projects
Stay updated on the latest advancements in machine learning technology
Incorporate new data sources to expand model functionality
Create detailed technical documentation for machine learning workflows and model designs
Present technical findings and model outcomes to team members and stakeholders
requirements
3+ years of experience in Machine Learning Engineering with expertise in AWS environments
Proficiency in Amazon Bedrock for creating and deploying machine learning solutions
Competency in using Amazon SageMaker for managing machine learning models
Understanding of Amazon S3 for efficient data storage and handling in machine learning applications
Advanced skills in Python for the development of machine learning models
Expertise in Natural Language Processing (NLP) and its applications
Background in Deep Learning and its practical implementation
Knowledge of Prompt Engineering to optimize model interaction
Experience conducting Sentiment Analysis in real-world scenarios
Qualifications in Model Evaluation for NLP with a focus on performance assessment
English proficiency at a B2 level or higher for effective communication
nice to have
Familiarity with other cloud platforms like Google Cloud or Microsoft Azure
Skills in programming languages such as R or Java
Relevant certifications in machine learning, data science, or related fields
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