machine engineer

Smart Connected Services for Technical Services LLC

Hiring Now: Pile Machine Engineer (Heavy Equipment / Construction)Location: UAE Job Type: Full-TimeWe are looking for an experienced and highly skilled Pile Machine Engineer to join our team for major upcoming infrastructure and heavy civil projects. The ideal candidate must have strong technical expertise in operating, troubleshooting, and maintaining pile driving machinery and related equipment.Key Responsibilities:<ul><li>Operate and manage Pile Driving Machines in accordance with project specifications and safety standards</li><li>Conduct regular inspections, maintenance, and troubleshooting of heavy equipment</li><li>Coordinate with site supervisors, engineers, and technicians to ensure timely execution of piling works</li><li>Supervise piling crew and ensure proper handling of equipment</li><li>Prepare daily work reports and ensure compliance with project timelines</li><li>Ensure all activities meet HSE standards and client requirements</li></ul>Requirements:<ul><li>Minimum 3&ndash;5 years of experience as a Pile Machine Engineer / Operator</li><li>Strong knowledge of Pile Drivers, Hydraulic Machines, and Heavy Equipment</li><li>Background in Civil Engineering, Mechanical Engineering, or related field</li><li>Experience in large construction or infrastructure projects is an advantage</li><li>Valid UAE Driving License (preferred)</li><li>Strong problem-solving and leadership skills</li><li>Ability to work under pressure and meet project deadlines</li></ul>Salary:Competitive package based on experienceHow to Apply:Send your updated CV to info@tawasolservices.com/ WhatsApp; 052 145 9366with the subject: Pile Machine Engineer ApplicationJob Type: Full-timePay: AED7,000.00 - AED8,000.00 per month

Pile Machine Engineer

Tata Consultancy Services (TCS)

<div><div><div><div><div><div><div><ul><li> Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.</li></ul>
<ul><li> Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.</li></ul>
<ul><li> Automate the training, testing and deployment processes for machine learning models.</li></ul>
<ul><li> Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.</li></ul>
<ul><li> Implement best practices for version control, model reproducibility and governance.</li></ul>
<ul><li> Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness.</li></ul>
<ul><li> Troubleshoot and resolve issues related to model deployment and performance.</li></ul>
<ul><li> Ensure compliance with security and data privacy standards in all MLOps activities.</li></ul>
<ul><li> Keep up-to-date with the latest MLOps tools, technologies and trends.</li></ul>
<ul><li> Provide support and guidance to other team members on MLOps practices.</li></ul>
</div></div> 
</div></div></div></div><div><div><div>Location</div>
<div>Chennai</div>
</div><div><div>Job Function</div>
<div>TECHNOLOGY</div>
</div><div><div>Role</div>
<div>Engineer</div>
</div><div><div>Job Id</div>
<div>381175</div>
</div><div><div>Desired Skills</div>
<div>Machine Learning</div>
</div></div>Desired Candidate Profile
<div><div>Qualifications : BACHELOR OF TECHNOLOGY</div></div></div><div></div>

Machine Learning Engineer

Comprehensive Rehab Consultants

About Comprehensive Rehab Consultants (CRC)Comprehensive Rehab Consultants partners with skilled nursing facilities to improve clinical quality, efficiency, and financial performance. CRC is recognized as an innovator in the post-acute care space, developing new AI-driven care and revenue models that decrease hospitalizations and improve outcomes.We are seeking a Machine Learning Engineer to drive the next generation of CRC&rsquo;s AI platform. This role is engineering-heavy, focused on building, tuning, and deploying scalable ML/NLP/LLM systems that power our clinician tools, financial analytics, contract modeling, DFS/PDPM engines, readmission risk scores, and enterprise analytics chatbot.Role SummaryYou will design, optimize, and productionize machine learning systems using CRC&rsquo;s full internal data environment. This includes tuning an existing NLP engine, developing statistical/ML models (including ordered probit and regression-based DFS calculator), building internal LLMs trained on CRC documents, and developing the AI analytics chatbot used by all business units.This role requires someone who ships ML systems&mdash;not someone who just builds notebooks.Key ResponsibilitiesMachine Learning &amp; Statistical Modeling<ul><li>Build, tune, and validate statistical models including multi-stage regression, ordered probit, and generalized linear models, audit automation, acuity scoring, and financial forecasting</li><li>Engineer features from structured and unstructured healthcare data (EMR, claims, revenue cycle, clinician notes)</li></ul>NLP &amp; LLM Engineering<ul><li>Tune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoring</li><li>Build custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector models</li><li>Develop and maintain a CRC private LLM, trained on internal knowledge bases, documentation, analytics logic, and care guidelines</li><li>Build automated pipelines for LLM evaluation, retraining, retrieval-augmented generation (RAG), and grounded QA</li></ul>AI-Driven Product Development<ul><li>Architect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sources</li><li>Integrate ML models into production services using notebooks, APIs, or batch inference jobs</li><li>Support creation of AI-generated reporting, insights summaries, and automated clinical/financial narratives</li></ul>MLOps &amp; Engineering<ul><li>Build maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CD</li><li>Implement model monitoring, drift detection, and automated retraining</li><li>Package and deploy reproducible models via APIs or scheduled Fabric/Databricks workflows</li></ul>Collaboration &amp; Delivery<ul><li>Work with data engineering to embed models into CRC applications</li><li>Partner with BI analysts to transform model outputs into dashboards</li><li>Document methodologies, assumptions, architecture, and validation processes clearly</li></ul>Required Skills &amp; Experience<ul><li>3&ndash;6 years of hands-on machine learning engineering experience (not just DS notebooks)</li><li>Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCy</li><li>Experience building and tuning LLM and NLP pipelines end-to-end</li><li>Experience with regression, ordered probit/logit, hierarchical models, and general statistical modeling</li><li>Experience deploying ML workloads in Databricks, Azure ML, and Fabric</li><li>Strong SQL for feature engineering and model validation</li><li>Prior experience working with healthcare data (EMR, claims, RCM, CMS) preferred</li><li>Strong communication and the ability to explain complex ML systems to non-technical stakeholders.</li><li>Proactive, self-managing engineer who can independently own ML systems end-to-end.</li><li>Fluent English required</li></ul>Preferred Qualifications<ul><li>Experience with:</li><li>Retrieval-Augmented Generation (RAG) pipelines</li><li>Vector databases (FAISS, Chroma, Pinecone, Qdrant)</li><li>Enterprise chatbot frameworks</li><li>MLflow, CI/CD, GitHub Actions, and model versioning</li><li>Power BI integration for ML outputs</li><li>FHIR/SMART on FHIR</li></ul>Certifications considered:<ul><li>Databricks ML Associate/Professional</li><li>Azure AI Engineer Associate</li><li>DeepLearning.AI NLP/LLM specializations</li></ul>Job Type: Full-timePay: From Rs3,030,000.00 per yearExperience:<ul><li>hands-on machine learning engineering: 3 years (Preferred)</li></ul>Language:<ul><li>English (Preferred)</li></ul>Work Location: Remote

About Comprehensive Rehab Consultants (CRC)Comprehensive Rehab Consultants partners with skilled nursing facilities to improve clinical quality, efficiency, and financial performance. CRC is recognized as an innovator in the post-acute care space, developing new AI-driven care and revenue models that decrease hospitalizations and improve outcomes.We are seeking a Machine Learning Engineer to drive the next generation of CRC&rsquo;s AI platform. This role is engineering-heavy, focused on building, tuning, and deploying scalable ML/NLP/LLM systems that power our clinician tools, financial analytics, contract modeling, DFS/PDPM engines, readmission risk scores, and enterprise analytics chatbot.Role SummaryYou will design, optimize, and productionize machine learning systems using CRC&rsquo;s full internal data environment. This includes tuning an existing NLP engine, developing statistical/ML models (including ordered probit and regression-based DFS calculator), building internal LLMs trained on CRC documents, and developing the AI analytics chatbot used by all business units.This role requires someone who ships ML systems&mdash;not someone who just builds notebooks.Key ResponsibilitiesMachine Learning &amp; Statistical Modeling<ul><li>Build, tune, and validate statistical models including multi-stage regression, ordered probit, and generalized linear models, audit automation, acuity scoring, and financial forecasting</li><li>Engineer features from structured and unstructured healthcare data (EMR, claims, revenue cycle, clinician notes)</li></ul>NLP &amp; LLM Engineering<ul><li>Tune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoring</li><li>Build custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector models</li><li>Develop and maintain a CRC private LLM, trained on internal knowledge bases, documentation, analytics logic, and care guidelines</li><li>Build automated pipelines for LLM evaluation, retraining, retrieval-augmented generation (RAG), and grounded QA</li></ul>AI-Driven Product Development<ul><li>Architect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sources</li><li>Integrate ML models into production services using notebooks, APIs, or batch inference jobs</li><li>Support creation of AI-generated reporting, insights summaries, and automated clinical/financial narratives</li></ul>MLOps &amp; Engineering<ul><li>Build maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CD</li><li>Implement model monitoring, drift detection, and automated retraining</li><li>Package and deploy reproducible models via APIs or scheduled Fabric/Databricks workflows</li></ul>Collaboration &amp; Delivery<ul><li>Work with data engineering to embed models into CRC applications</li><li>Partner with BI analysts to transform model outputs into dashboards</li><li>Document methodologies, assumptions, architecture, and validation processes clearly</li></ul>Required Skills &amp; Experience<ul><li>3&ndash;6 years of hands-on machine learning engineering experience (not just DS notebooks)</li><li>Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCy</li><li>Experience building and tuning LLM and NLP pipelines end-to-end</li><li>Experience with regression, ordered probit/logit, hierarchical models, and general statistical modeling</li><li>Experience deploying ML workloads in Databricks, Azure ML, and Fabric</li><li>Strong SQL for feature engineering and model validation</li><li>Prior experience working with healthcare data (EMR, claims, RCM, CMS) preferred</li><li>Strong communication and the ability to explain complex ML systems to non-technical stakeholders.</li><li>Proactive, self-managing engineer who can independently own ML systems end-to-end.</li><li>Fluent English required</li></ul>Preferred Qualifications<ul><li>Experience with:</li><li>Retrieval-Augmented Generation (RAG) pipelines</li><li>Vector databases (FAISS, Chroma, Pinecone, Qdrant)</li><li>Enterprise chatbot frameworks</li><li>MLflow, CI/CD, GitHub Actions, and model versioning</li><li>Power BI integration for ML outputs</li><li>FHIR/SMART on FHIR</li></ul>Certifications considered:<ul><li>Databricks ML Associate/Professional</li><li>Azure AI Engineer Associate</li><li>DeepLearning.AI NLP/LLM specializations</li></ul>Job Type: Full-timePay: From &#8377;949,364.77 per yearBenefits:<ul><li>Paid time off</li></ul>Experience:<ul><li>hands-on machine learning engineering: 3 years (Preferred)</li></ul>Language:<ul><li>English (Preferred)</li></ul>

Rough Copies Pvt Ltd

Location: Johar Town, Lahore Timing: 7 PM &ndash; 2 AMAbout the Role: We are hiring a Machine Learning Engineer with hands-on experience in Recommendation Systems and TensorFlow. You will design, train, and deploy ML models, optimize pipelines, and work on real-world AI applications.Key Responsibilities:<ul><li>Build and optimize recommendation systems (collaborative/content-based)</li><li>Develop ML models using TensorFlow</li><li>Integrate ML models into production pipelines</li><li>Collaborate with cross-functional teams and deliver results on time</li></ul>Requirements:<ul><li>2&ndash;3 years of professional ML experience</li><li>Strong Python, NumPy, Pandas skills</li><li>Experience with TensorFlow and model deployment</li><li>Knowledge of recommendation systems</li><li>Ability to work independently in a night shift environment</li></ul>Preferred:<ul><li>API integration experience</li><li>Experience with real-time ML systems</li></ul>Job Type: Full-timeApplication Question(s):<ul><li>Do you have experience building recommendation systems?</li><li>Have you worked with TensorFlow for model building and deployment?</li></ul>Experience:<ul><li>Machine Learning : 2 years (Required)</li></ul>Work Location: In person

HCLTech

<div>
 
 
 Job Title:
 
 
 
 
 ML Ops Engineer / ML Engineer
 
 
 
 Experience - 5Yrs -20 Yrs
 
 
 Location - Chennai / Bangalore / Hyderabad / Pune / Noida / Mumbai/
 
 
 
 
 
 
 Job Overview:
 
 
 
 We are looking for an experienced MLOps Engineer to help deploy, scale, and manage machine learning models in production environments. You will work closely with data scientists and engineering teams to automate the machine learning lifecycle, optimize model performance, and ensure smooth integration with data pipelines.
 
 
 
 
 
 
 Key Responsibilities:
 
 
 
 
 Transform prototypes into production-grade models
 
 
 <ul>
 <li>
 Assist in building and maintaining machine learning pipelines and infrastructure across cloud platforms such as AWS, Azure, and GCP.
 </li>
 <li>
 Develop REST APIs or FastAPI services for model serving, enabling real-time predictions and integration with other applications.
 </li>
 <li>
 Collaborate with data scientists to design and develop drift detection and accuracy measurements for live models deployed.
 </li>
 <li>
 Collaborate with data governance and technical teams to ensure compliance with engineering standards.
 </li>
 </ul>
 
 
 Maintain models in production
 
 
 <ul>
 <li>
 Collaborate with data scientists and engineers to deploy, monitor, update, and manage models in production.
 </li>
 <li>
 Manage the full CI/CD cycle for live models, including testing and deployment.
 </li>
 <li>
 Develop logging, alerting, and mitigation strategies for handling model errors and optimize performance.
 </li>
 <li>
 Troubleshoot and resolve issues related to ML model deployment and performance.
 </li>
 <li>
 Support both batch and real-time integrations for model inference, ensuring models are accessible through APIs or scheduled batch jobs, depending on use case.
 </li>
 </ul>
 
 
 Contribute to AI platform and engineering practices
 
 
 <ul>
 <li>
 Contribute to the development and maintenance of the AI infrastructure, ensuring the models are scalable, secure, and optimized for performance.
 </li>
 <li>
 Collaborate with the team to establish best practices for model deployment, version control, monitoring, and continuous integration/continuous deployment (CI/CD).
 </li>
 <li>
 Drive the adoption of modern AI/ML engineering practices and help enhance the team&rsquo;s MLOps capabilities.
 </li>
 <li>
 Develop and maintain Flask or FastAPI-based microservices for serving models and managing model APIs.
 </li>
 </ul>
 
 
 
 
 
 Minimum Required Skills:
 
 
 <ul>
 <li>
 Bachelor's degree in computer science, analytics, mathematics, statistics.
 </li>
 <li>
 Strong experience in Python, SQL, Pyspark.
 </li>
 <li>
 Solid understanding and knowledge of containerization technologies (Docker, Podman, Kubernetes).
 </li>
 <li>
 Proficient in CI/CD pipelines, model monitoring, and MLOps platforms (e.g., AWS SageMaker, Azure ML, MLFlow).
 </li>
 <li>
 Proficiency in cloud platforms, specifically AWS, Azure and GCP.
 </li>
 <li>
 Familiarity with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
 </li>
 <li>
 Familiarity with batch processing integration for large-scale data pipelines.
 </li>
 <li>
 Experience with serving models using FastAPI, Flask, or similar frameworks for real-time inference.
 </li>
 <li>
 Certifications in AWS, Azure or ML technologies are a plus.
 </li>
 <li>
 Experience with Databricks is highly valued.
 </li>
 <li>
 Strong problem-solving and analytical skills.
 </li>
 <li>
 Ability to work in a team-oriented, collaborative environment.
 </li>
 </ul>
 
 
 
 
 
 Tools and Technologies:
 
 
 
 
 Model Development &amp; Tracking:
 
 TensorFlow, PyTorch, scikit-learn, MLflow, Weights &amp; Biases
 
 
 
 Model Packaging &amp; Serving:
 
 Docker, Kubernetes, FastAPI, Flask, ONNX, TorchScript
 
 
 
 CI/CD &amp; Pipelines:
 
 GitHub Actions, GitLab CI, Jenkins, ZenML, Kubeflow Pipelines, Metaflow
 
 
 
 Infrastructure &amp; Orchestration:
 
 Terraform, Ansible, Apache Airflow, Prefect
 
 
 
 Cloud &amp; Deployment:
 
 AWS, GCP, Azure, Serverless (Lambda, Cloud Functions)
 
 
 
 Monitoring &amp; Logging:
 
 Prometheus, Grafana, ELK Stack, WhyLabs, Evidently AI, Arize
 
 
 
 Testing &amp; Validation:
 
 Pytest, unittest, Pydantic, Great Expectations
 
 
 
 Feature Store &amp; Data Handling:
 
 Feast, Tecton, Hopsworks, Pandas, Spark, Dask
 
 
 
 Message Brokers &amp; Data Streams:
 
 Kafka, Redis Streams
 
 
 
 Vector DB &amp; LLM Integrations (optional):
 
 Pinecone, FAISS, Weaviate, LangChain, LlamaIndex, PromptLayer
 
</div>

TMC Middle East

<div>
 
 
 About the Role:
 
 
 
 We are looking for a Senior Machine Learning Engineer to lead the design, deployment, and optimization of machine learning and generative AI solutions. The role focuses on applying advanced ML techniques to solve real-world challenges across government and enterprise clients in Saudi Arabia.
 
 
 
 
 
 
 What You'll Do:
 
 
 <ul>
 <li>
 Design, build, and scale machine learning models and infrastructure.
 </li>
 <li>
 Deploy deep learning and generative models into production for public and private sector projects.
 </li>
 <li>
 Collaborate with data scientists, product managers, and stakeholders to deliver AI-driven insights.
 </li>
 <li>
 Optimize ML training and inference for performance, latency, and scalability.
 </li>
 <li>
 Monitor, retrain, and improve deployed ML models.
 </li>
 <li>
 Drive consultation efforts, providing expert guidance to client stakeholders.
 </li>
 </ul>
 
 
 
 
 
 Qualifications:
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science or related field.
 </li>
 <li>
 4+ years of experience in ML engineering, data science, and generative AI.
 </li>
 <li>
 Strong track record of projects across public and private sectors.
 </li>
 <li>
 Expertise in Python, TensorFlow, PyTorch, ONNX, and ML deployment frameworks.
 </li>
 <li>
 Expertise in containerization and orchestration technologies (Docker, Kubernetes), with hands-on experience in ML lifecycle management (MLflow) and local LLM deployment frameworks (Ollama).
 </li>
 <li>
 Skilled in on-premise infrastructure design and enterprise architecture frameworks, with strong adherence to security and compliance best practices.
 </li>
 <li>
 Deep understanding of hardware provisioning for local LLM deployments (GPU, RAM, and storage optimization) and secure network configurations for internal AI services.
 </li>
 <li>
 Experience with cloud ML environments (SageMaker, Vertex AI, OCI Data Science).
 </li>
 <li>
 Familiarity with MLOps practices and automation pipelines.
 </li>
 </ul>
 
 
</div>

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