7 Ml Engineer Jobs

Prescience Decision Solutions is looking for ML Engineer to join our dynamic team and embark on a rewarding career journey. We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The Machine Learning Engineer will be responsible for designing, developing, and deploying machine learning models to solve complex problems and enhance our products or services. The ideal candidate will have a strong background in machine learning algorithms, programming, and data analysis. Responsibilities : Problem Definition : Collaborate with cross - functional teams to define and understand business problems suitable for machine learning solutions. Translate business requirements into machine learning objectives. Data Exploration and Preparation : Analyze and preprocess large datasets to extract relevant features for model training. Address data quality issues and ensure data readiness for machine learning tasks. Model Development : Develop and implement machine learning models using state - of - the - art algorithms. Experiment with different models and approaches to achieve optimal performance. Training and Evaluation : Train machine learning models on diverse datasets and fine - tune hyperparameters. Evaluate model performance using appropriate metrics and iterate on improvements. Deployment : Deploy machine learning models into production environments. Collaborate with DevOps and IT teams to ensure smooth integration. Monitoring and Maintenance : Implement monitoring systems to track model performance in real - time. Regularly update and retrain models to adapt to evolving data patterns. Documentation : Document the entire machine learning development pipeline, from data preprocessing to model deployment. Create user guides and documentation for end - users and stakeholders. Collaboration : Collaborate with data scientists, software engineers, and domain experts to achieve project goals. Participate in cross - functional team meetings and knowledge - sharing sessions.

Job role & responsibilities:- Collaborate with different teams to propose AI solutions on different use cases across the insurance value chain, with a focus on AIops and MLOps Research, build, and deploy AI models as part of the broader AI team, leveraging AIops and MLOps practices for efficient model management Contribute to our DevOps practices using OpenShift or Azure ML DevOps Technical Skills, Experience & Qualification required:- Expertise is required in the following fields: 6-9 years of progressive experience in AI and ML, with a focus on AIops and MLOps Experience in ML Flow or Cube Flow or Airflow, ML Ops, more in to production deployment Experience in deploying and managing AI models in production environments using Azure ML DevOps or OpenShift Implementation of at least 5 AI projects, preferably with experience in AIops and MLOps Experience with Azure, OpenShift, MLFlow DevOps for model deployment, monitoring, and management Setting up CI/CD pipelines using Azure DevOps, Jenkins, etc. Hands-on experience with Generative AI tech LLMs, RAG, Prompt Engineering Broad understanding of machine learning algorithms and techniques, including LLMs/SLMs, CNNs) RNNs, transformers, and attention mechanisms Immediate Joiners will be preferred

Role & responsibilities Data Scientist /ML engineers : ML Engineer with Python, SQL, Machine Learning, Azure

Work closely with the ML Architect to develop on ML frameworks (TensorFlow, Scikit-Learn, Pytorch) Strong background in MLOps practices, including CI/CD, containerization (Docker), Orchestration frameworks (Kubernetes, Airflow)

Dear Candidate Greetings from A2Z HR Consultants !!!!!!!! We are hiring for one of the renowned Web Software company based in Noida Number of working days: 5 Shift Timings: Day Shifts Salary: upto 32 LPA Profile: AI/ ML Engineer Experience Required: Min 5 Years **** Work from Office only Job Summary: Join our forward-thinking team to pioneer cutting-edge AI solutions that transform industries. We seek an AI Expert with 5+ years of experience in Python, machine learning, and large language models (LLMs), paired with robust MLOps expertise. You will architect, optimize, and deploy scalable AI systems, focusing on LLM fine-tuning (e.g., Llama, GPT, Mistral), Retrieval-Augmented Generation (RAG), and production-grade deployment on AWS . If you thrive on solving complex challenges, driving ethical AI innovation, and leading cross-functional teams, this role is for you. Key Responsibilities: Design, develop, and deploy AI/ML models using Python and relevant frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). Optimize and fine-tune machine learning algorithms for performance, scalability, and accuracy. Work with large datasets to extract insights, preprocess data, and build predictive models. Develop and integrate AI-powered solutions into applications, including natural language processing (NLP), computer vision, and deep learning systems. Architect, fine-tune, and deploy large language models (LLMs) for various use cases such as chatbots, text generation, summarization, and document understanding. Implement retrieval-augmented generation (RAG) techniques to enhance LLM capabilities. Research and apply model compression techniques such as quantization and distillation to optimize LLM deployment. Leverage embeddings, knowledge graphs, and vector databases for efficient information retrieval and AI-driven insights. Develop robust MLOps/LLMOps pipelines for model versioning, monitoring, and CI/CD integration. Deploy AI models in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML) and optimize inference cost-performance trade-offs. Utilize containerization and orchestration tools such as Docker, Kubernetes, and Kubeflow for scalable AI deployments. Stay updated on the latest AI advancements and integrate emerging technologies into production systems. Ensure AI model interpretability, fairness, and adherence to ethical AI principles. Participate in code reviews, debugging, and troubleshooting of AI models and pipelines. Required Qualifications & Skills: Education: Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Experience: 5+ years of hands-on experience in AI, machine learning, or deep learning projects. Programming: Strong proficiency in Python and its AI/ML libraries (NumPy, Pandas, TensorFlow, PyTorch, Scikit-learn, etc.). LLM Expertise: Hands-on experience with LLMs such as OpenAIs GPT, Llama, Mistral, Gemini, PaLM, or similar frameworks. Fine-Tuning & Optimization: Experience fine-tuning LLMs, optimizing for cost-performance balance, and utilizing techniques like LoRA, PEFT, and RLHF. NLP & Deep Learning: Expertise in NLP model training, transformer-based architectures (BERT, T5, GPT, etc.), and model evaluation techniques. MLOps & LLMOps: Experience with model lifecycle management, monitoring, CI/CD pipelines, and cloud-based model deployment. Cloud & Deployment: Proficiency in deploying AI models on Google Cloud (Vertex AI), AWS, or Azure. Containerization & Orchestration: Experience with Docker, Kubernetes, and Kubeflow for AI model deployment. Data Engineering: Knowledge of data preprocessing, feature engineering, and handling large-scale datasets efficiently. Prompt Engineering: Strong understanding of prompt design, embedding generation, and model evaluation metrics for LLMs. Security & Ethics: Familiarity with AI security best practices, data privacy, and responsible AI principles. Interested candidates can reach out at 9711831492 or share your resume at gaurav.a2zhrconsultants@gmail.com Candidates who are already on Notice period or immediately available shall apply only. Regards Gaurav Kumar A2Z HR Consultants 9711831492

We are RadarRadar , experts in the commodity production, trade and processing industry. As a technology company we continuously aim to support our clients with strong data C analytics and business intelligence tools. It is our mission to enable companies to unlock the full potential of their data to improve risk and margin management and boost performance. As a Machine Learning (ML) Engineer you will work alongside an engineering team that builds RadarRadar machine learning tools for our clients. We are looking for an individual with deep experience in designing, implementing, assessing, and refining machine learning models. You love to work- and think progressively, because as first movers we need to stay ahead of market trends and deliver first class products. What will you do: Design and implement ML/AI solutions to optimize and enhance the RadarRadar product suite. Adhering to high quality development principles while delivering solutions on time. Participate in scrum related activities (stand-ups, planning, demo sessions, etc.) Work closely with cross-functional teams, including product, and engineering, to align ML/AI capabilities with business goals. Create technical documentation and specifications for feature integrations. Conduct research/implementation of latest industry standard best practices. Break down, estimate, and implement features containing multiple tasks. Review, assess and improve the full set of activities presented above. What will you bring: 3+ years of professional experience as a ML engineer or a similar role. Proficiency with state-of-the-art ML models and frameworks (e.g., Keras, NumPy, scikit-learn, PyTorch, TensorFlow). Knowledge/interest in current progressions in the AI world and interest in enterprise level development of AI models using LLM APIs (e.g. OpenAI, Gemini, Claude, Anthropic). Expertise in hyperparameter tuning to optimize model performance. Familiarity with Explainable AI (XAI) techniques. Advanced Python programming skills (min. 2 years of experience). T-SQL (SQL Server) proficient level of knowledge (min. 1-2 years), experience working with stored procedures, functions, triggers, indexes, dynamic SQL, query performance tuning. Comfortable with complexity and pursuit of excellent results, willing to learn on the job. A proactive thinker who can work independently and bring innovative ideas. Balances hard skills with interpersonal ability. Strong knowledge of clean code principles. Ability to work in a collaborative manner. What you will get: Rotterdam-based hybrid workplace model A competitive salary and working with an amazing team. Flexible working hours and your own laptop. Friday drinks and (virtual) team events. Company merchandise. An inspiring environment where you learn every day. Personal development plan to help you reach your personal goals.

Looking for 5 to 10 years experience of ML Engineer with strong Azure Cloud DevOps with even stronger DABs DevOps skills with even stronger DABs Databricks Asset Bundles implementation knowledge Translate business requirement into technical solution Implementation of MLOPS Scalable solution using AIML and reduce the risk of Fraud and other fiscal crisis Creating MLOPS Architecture and implementing it for multiple models in a scalable and automated way Designing and implementing end to end ML solutions Operationalize and monitor machine learning models using high end tools and technologies Design implementation of DevOps principles in Machine Learning Data Science quality assurance and testing Collaborate with data scientists engineers and other key stakeholders Key Responsibilities Azure Cloud Engineering Design implement and manage scalable cloud infrastructure on Microsoft Azure Ensure high availability performance and security of cloud based applications Collaborate with cross functional teams to define and implement cloud solutions Develop and maintain CICD pipelines using Azure DevOps Automate deployment processes to ensure efficient and reliable software delivery Monitor and troubleshoot CICD pipelines to ensure smooth operation DABs Databricks Asset Bundles Implementation Lead the implementation and management of Databricks Asset Bundles Optimize data workflows and ensure seamless integration with existing systems Provide expertise in DABs to enhance data processing and analytics capabilities Machine Learning Deployment Deploy machine learning models into production environments Monitor and maintain ML models to ensure optimal performance Collaborate with data scientists and engineers to integrate ML solutions into applications