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CAE Analyst Job Description Job Summary We are seeking a skilled and experienced CAE Analyst to join our team in Chennai. This role is crucial for supporting our Automotive / Mechanical Engineering initiatives, ensuring the quality and durability of our components and systems. Key Responsibilities The CAE Analyst will be responsible for performing a wide range of CAE simulations and analyses to support product development and validation. This includes, but is not limited to, conducting structural analysis , stress analysis , and fatigue life prediction . The role will also involve root cause analysis and failure analysis to identify and resolve potential issues. The analyst will work closely with design and validation teams to ensure that designs meet performance and durability requirements. Required Skills & Qualifications The ideal candidate will possess a strong foundation in Mechanical Engineering and a proven ability to apply CAE principles to solve real-world engineering problems. A B.E / B.Tech / M.E / M.Tech degree is required. Non-linear FE analysis: Proficiency in performing advanced finite element analyses, including non-linear simulations to accurately predict component behavior under complex loading conditions. Abaqus: Demonstrated experience using Abaqus for conducting FE simulations. FE model building: Ability to create accurate and efficient FE models representing complex geometries and boundary conditions. Meshing: Expertise in generating high-quality meshes using industry-standard tools. HyperMesh: Experience utilizing HyperMesh for efficient mesh generation and model preparation. ANSA: Familiarity with ANSA for pre- and post-processing of FE models. Fatigue life prediction: Ability to perform fatigue life prediction using established methodologies and software. FEMFAT: Experience with FEMFAT for fatigue analysis and damage assessment. Structural analysis: A strong understanding of structural analysis principles and their application to automotive components. Stress analysis: Ability to perform detailed stress analysis to identify areas of high stress concentration and potential failure. Material modeling: Knowledge of material modeling techniques and their impact on simulation results. Contact definitions: Ability to accurately define and implement contact definitions in FE models. Reliability assessments: Experience in performing reliability assessments to evaluate the long-term performance of components. Durability assessments: Ability to conduct durability assessments to ensure components can withstand expected usage conditions. Root cause analysis: Proven ability to perform root cause analysis to identify the underlying causes of failures. Failure analysis: Experience in conducting failure analysis to determine the mechanisms of failure. Automotive engineering: A solid understanding of Automotive engineering principles and practices. Quality processes: Familiarity with quality processes and their application to product development. Robust design methodologies: Knowledge of robust design methodologies to create designs that are insensitive to variations in manufacturing and operating conditions. Manufacturing best practices: Understanding of manufacturing best practices and their impact on component performance. Transmission & driveline systems: Familiarity with Transmission & driveline systems and their components. Gears, Shafts, Clutches, Differentials, Propshafts, Half-shafts, Bearings: Knowledge of the design and analysis of these key components. HyperWorks: Experience with HyperWorks for CAE simulation and analysis. Fatigue life/Damage assessment: Ability to perform fatigue life/Damage assessment to predict component lifespan. Boundary conditions: Ability to accurately define and apply appropriate boundary conditions in FE models. Dynamic loading scenarios: Experience in simulating dynamic loading scenarios to evaluate component response to transient loads. Gear strength: Ability to assess gear strength and durability. Shaft durability: Ability to assess shaft durability under various loading conditions. Road load data: Experience in utilizing road load data for simulation. Simulation load cases: Ability to develop and apply appropriate simulation load cases . Physical test data: Ability to correlate simulation results with physical test data . Refine models: Ability to refine models based on simulation results and experimental data. Material properties: Understanding of material properties and their impact on simulation results. Manufacturing processes: Familiarity with manufacturing processes such as Forging, Casting, and Heat treatment . Component durability: Ability to assess component durability under various operating conditions. CAE simulation tools: Proficiency in using various CAE simulation tools . Customer usage profiles: Ability to incorporate customer usage profiles into simulation models. Duty cycles: Ability to define and apply appropriate duty cycles in simulations. Design robustness: Ability to assess and improve design robustness . DFMEA: Familiarity with DFMEA (Design Failure Mode and Effects Analysis). DVP&R: Understanding of DVP&R (Design Verification Plan and Release). Experience Required: The role requires 4 - 9 years of relevant experience in CAE analysis, preferably within the Automotive or Mechanical Engineering industry. Candidates with a strong track record of solving complex engineering problems and delivering high-quality results are highly encouraged to apply. What We Offer Salary: ₹10 LPA - ₹12 LPA
Job Summary We are seeking a skilled and motivated Software Engineer to join our dynamic team. This is an onsite position within the IT / Technology industry. Key Responsibilities This role will involve a range of responsibilities related to software development and maintenance, leveraging a full stack skillset to contribute to our engineering design and manufacturing processes. Required Skills & Qualifications Teamcenter Development: Demonstrated proficiency in developing and maintaining applications within the Teamcenter environment. Java: Strong understanding and practical experience with Java programming language. Full Stack Development: Ability to work across the entire software development lifecycle, from front-end to back-end. GCP: Familiarity with Google Cloud Platform (GCP) for cloud-based solutions. ITK: Experience with ITK (Integration Toolkit) for seamless integration of systems. SOA: Solid understanding of Service Oriented Architecture (SOA) principles and implementation. BMIDE: Experience with Business Modeler IDE for efficient business process modeling. Business Modeler IDE: Ability to utilize Business Modeler IDE to design and implement business processes. XRT: Knowledge of XRT related functionalities. Engineering Design: Understanding of Engineering Design principles and their application in software development. Manufacturing: Knowledge of Manufacturing processes and their integration with software systems. Active Workspace: Experience with Active Workspace for user interaction and data management. Data Models: Ability to design and implement robust Data Models . Business Objects: Experience working with Business Objects and their manipulation. Attributes: Understanding of Attributes and their role in data management. GRM Rules: Ability to configure and manage GRM (Group Rules Management) rules. LOVs: Experience with LOVs (Lists of Values) and their implementation. Declarative UI Components: Ability to develop and utilize Declarative UI Components for user interfaces. Root Cause Analysis: Strong analytical skills with the ability to perform Root Cause Analysis to identify and resolve issues. Database Queries: Proficiency in writing and executing Database Queries for data retrieval and manipulation. PLM Best Practices: Knowledge of PLM (Product Lifecycle Management) Best Practices . Integration Toolkit: Experience with Integration Toolkit for system integration. Web Services: Understanding of Web Services and their implementation. Qualifications: A Bachelor's Degree is required. Experience Required: 4 - 5 years of relevant experience are expected, demonstrating a solid foundation in software engineering principles and practices. This level of experience indicates a readiness to contribute independently and collaboratively within a team. What We Offer Salary: 15 LPA - 16 LPA
Machine Learning Engineer Job Description Job Summary We are seeking a skilled and motivated Machine Learning Engineer to join our growing team in Chennai. This role offers an exciting opportunity to contribute to cutting-edge projects within the IT / Technology industry. Key Responsibilities The Machine Learning Engineer will be responsible for designing, developing, and deploying machine learning models and pipelines. This includes collaborating with cross-functional teams to identify and solve complex business problems using data-driven solutions. Required Skills & Qualifications AI/ML : A strong understanding of artificial intelligence and machine learning principles, including various algorithms and techniques. Scikit-learn : Proficiency in utilizing Scikit-learn for model building, evaluation, and selection. PyTorch : Experience with PyTorch for deep learning model development and training. TensorFlow : Familiarity with TensorFlow for building and deploying large-scale machine learning models. LLM : Knowledge of Large Language Models (LLMs) and their applications. ETL : Experience with Extract, Transform, Load ( ETL ) processes for data preparation and integration. Big Query : Ability to work with Big Query for data warehousing and querying. Data Flow : Understanding of Data Flow concepts and their application in data processing. Python : Strong programming skills in Python for data analysis, machine learning, and scripting. Airflow : Experience with Airflow for orchestrating and scheduling data pipelines. PySpark : Proficiency in PySpark for distributed data processing and machine learning. SQL : Expertise in SQL for data querying and manipulation. GCS : Familiarity with Google Cloud Storage ( GCS ) for data storage and management. GitHub : Experience using GitHub for version control and collaboration. CI/CD : Knowledge of Continuous Integration and Continuous Deployment ( CI/CD ) practices. Dynatrace : Experience with Dynatrace for application performance monitoring. Analytical Skills : Exceptional analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions. Software Engineering : Solid foundation in software engineering principles and best practices. Design Practices : Understanding of software design patterns and principles. Documentation : Ability to create clear and concise technical documentation. DevOps : Familiarity with DevOps methodologies and tools. Pair Programming : Experience with Pair Programming techniques for collaborative development. TDD : Knowledge of Test-Driven Development ( TDD ) principles. Agile Ceremonies : Experience participating in Agile Ceremonies . PI Planning : Understanding of PI Planning within an Agile framework. Experience Required: 4 - 10 years of relevant experience. We are seeking a candidate with a proven track record of building and deploying machine learning solutions. This level of experience should demonstrate a strong understanding of the entire machine learning lifecycle, from data collection and preparation to model deployment and monitoring. What We Offer Salary:
Job Summary We are seeking a skilled and motivated Machine Learning Engineering Engineer 2 to join our growing team. This role is ideal for an individual with a strong foundation in machine learning principles and a passion for building and deploying innovative solutions within the IT / Technology industry. Key Responsibilities The responsibilities of this role will encompass a range of tasks related to the development, implementation, and maintenance of machine learning models and pipelines. The candidate will collaborate with cross-functional teams to ensure the successful integration of machine learning solutions into our existing infrastructure. Required Skills & Qualifications AI/ML: Demonstrated proficiency in Artificial Intelligence and Machine Learning concepts, including model selection, training, evaluation, and deployment. Scikit-learn: Experience utilizing Scikit-learn for various machine learning tasks, including data preprocessing, model selection, and evaluation. PyTorch: Familiarity with PyTorch , a widely used deep learning framework, for building and training complex neural networks. TensorFlow: Experience with TensorFlow , another popular deep learning framework, for developing and deploying machine learning models at scale. LLM: Understanding of Large Language Models ( LLM ) and their applications. ETL: Knowledge of ETL (Extract, Transform, Load) processes for data preparation and integration. Big Query: Experience with BigQuery for querying and analyzing large datasets. Data Flow: Familiarity with Data Flow for building and managing data processing pipelines. Python: Strong programming skills in Python , a versatile language widely used in machine learning. Airflow: Experience with Airflow for orchestrating and scheduling complex data workflows. PySpark: Proficiency in PySpark for distributed data processing and machine learning. SQL: Solid understanding of SQL for data querying and manipulation. GCS: Experience with GCS (Google Cloud Storage) for storing and managing data in the cloud. GitHub: Familiarity with GitHub for version control and collaborative development. CI/CD: Understanding of CI/CD (Continuous Integration/Continuous Deployment) principles and practices. Dynatrace: Experience with Dynatrace for application performance monitoring. Analytical Skills: Strong analytical and problem-solving skills, with the ability to interpret data and draw meaningful conclusions. Software Engineering: Solid foundation in Software Engineering principles and best practices. Design Practices: Knowledge of software design patterns and principles. Documentation: Ability to create clear and concise technical documentation. DevOps: Understanding of DevOps methodologies and practices. Pair Programming: Experience with Pair Programming techniques for collaborative code development. TDD: Familiarity with TDD (Test-Driven Development) methodologies. Agile: Experience working within an Agile development environment. Education: Bachelor's Degree required. Experience: 1 - 4 years of relevant experience. This level is expected to be a mid-level contributor, capable of working independently and contributing to team goals. What We Offer Salary:
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