5 Seldon Jobs

Lead the development of scalable and efficient machine learning platform products. Develop technical specifications and architectures for new ML platform features and products. Collaborate with cross-functional teams across the globe to ensure product development aligns with business goals. Define and enforce engineering best practices to ensure high-quality deliverables. Participate in the code review process to ensure our code quality standards are met. Stay up-to-date with the latest ML platform technologies and trends to identify opportunities for product innovation. Participate in the hiring and onboarding process for new team members Communicate effectively with technical and non-technical stakeholders to ensure alignment on project goals and timelines. Required Skills and Experience - Candidate should have a Bachelors or Masters in Computer Science or equivalent Demonstrate Technical leadership with hands-on coding experience. 9+ years of experience of commercial software development with at least 3+ years working within the Machine Learning / AI space, Demonstrated excellence participating on cross functional teams in fast-paced environments, both in terms of technical leadership and hands-on coding. Excellent ability to break down complex problems into simple solutions. Willingness and ability to learn, evaluate, and make recommendations for leveraging new technologies. Strong analytical skills and desire to write clean, correct and efficient code. Sense of ownership, urgency and pride in your work. Proven that you are a leader who prioritizes, communicates clearly, and partners effectively with both technical and nontechnical employees. Excellent command of tools and expertise for troubleshooting production issues. Experience working with Python, Docker, Kubernetes. Experience with Kubeflow, Seldon, Spark and AWS / Azure cloud service experience is a plus.

About Position As a crucial member of our team, you'll play a pivotal role across the entire machine learning lifecycle, contributing to our conversational AI bots, RAG system and traditional ML problem solving for our observability platform. Your tasks will encompass both operational and engineering aspects, including building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation processes. On the LLM side you'll fine-tune generative AI models, design agentic language chains, and prototype recommender system experiments. Role : AL ML Engineer Location: Bengaluru, Hyderabad Experience: 2-6 years What You'll Do Fine-tuning generative AI models to enhance performance. Designing AI Agents for conversational AI applications. Experimenting with new techniques to develop models for observability use cases Building and maintaining inference pipelines for efficient model deployment. Managing deployment and model versioning pipelines for seamless updates. Developing tooling to continuously validate models in production environments. What we're looking for: 2-6 Years Demonstrated proficiency in software engineering design practices. Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree (Master's or Ph.D.) preferred. Experience working with transformer models and text embeddings. Proven track record of deploying and managing ML models in production environments. Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy. Preferred experience developing production-grade applications in Python. Proficiency in Kubernetes and containers. Familiarity with concepts/libraries such as sklearn, kubeflow, argo, and seldon. Expertise in Python, C++, Kotlin, or similar programming languages. Experience designing, developing, and testing scalable distributed systems. Familiarity with message broker systems (e.g., Kafka, RabbitMQ). Knowledge of application instrumentation and monitoring practices. Experience with ML workflow management, like AirFlow, Sagemaker, etc. Familiarity with the AWS ecosystem. Past projects involving the construction of agentic language chains. Benefits Competitive salary and benefits package Culture focused on talent development with quarterly promotion cycles and company sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards, Annual health, check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Note: We are Preferring candidates from premium institutes and product firms

About Position As a crucial member of our team, you'll play a pivotal role across the entire machine learning lifecycle, contributing to our conversational AI bots, RAG system and traditional ML problem solving for our observability platform. Your tasks will encompass both operational and engineering aspects, including building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation processes. On the LLM side you'll fine-tune generative AI models, design agentic language chains, and prototype recommender system experiments. Role : AL ML Engineer Location: Bengaluru, Hyderabad Experience: 2-6 years What You'll Do Fine-tuning generative AI models to enhance performance. Designing AI Agents for conversational AI applications. Experimenting with new techniques to develop models for observability use cases Building and maintaining inference pipelines for efficient model deployment. Managing deployment and model versioning pipelines for seamless updates. Developing tooling to continuously validate models in production environments. What we're looking for: 2-6 Years Demonstrated proficiency in software engineering design practices. Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree (Master's or Ph.D.) preferred. Experience working with transformer models and text embeddings. Proven track record of deploying and managing ML models in production environments. Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy. Preferred experience developing production-grade applications in Python. Proficiency in Kubernetes and containers. Familiarity with concepts/libraries such as sklearn, kubeflow, argo, and seldon. Expertise in Python, C++, Kotlin, or similar programming languages. Experience designing, developing, and testing scalable distributed systems. Familiarity with message broker systems (e.g., Kafka, RabbitMQ). Knowledge of application instrumentation and monitoring practices. Experience with ML workflow management, like AirFlow, Sagemaker, etc. Familiarity with the AWS ecosystem. Past projects involving the construction of agentic language chains. Benefits Competitive salary and benefits package Culture focused on talent development with quarterly promotion cycles and company sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards, Annual health, check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Note: We are Preferring candidates from premium institutes and product firms

Please apply using the link here: https://saarthee.keka.com/careers/jobdetails/72540 Position Summary: We are seeking a skilled Machine Learning Engineer to design, develop, and deploy ML models for various applications, including recommendation systems. The ideal candidate will implement and optimize machine learning algorithms to enhance model performance and accuracy while ensuring that enterprise infrastructure and data pipelines are equipped to support scalable ML solutions. This role involves collaborating with ML engineers and stakeholders to translate business requirements into effective ML solutions. Responsibilities include designing scalable machine learning pipelines for data preprocessing, model training, and deployment, as well as enhancing model monitoring to track scalability and error rates. Additionally, the role focuses on ML model load testing, developing end-to-end test cases, and evaluating model scalability and latency under varying workloads. The candidate will automate test cases to ensure seamless model deployment and operation in production environments. Responsibilities: Design and develop machine learning models for various applications, such as recommendation systems. Deploy machine learning solutions to meet enterprise goals and support experimentation and innovation. Ensure that enterprise infrastructure and data pipelines are well-equipped to support machine learning solutions. Implement and optimize machine learning algorithms to improve model performance and accuracy. Collaborate with ML engineers, and stakeholders to understand business requirements and translate them into effective ML solutions. Design and implement scalable machine learning pipelines for data preprocessing, model training, and deployment. Enhance Monitoring of model scalability, and incident of increased error rate and ensure optimal results. Focus on ML model load testing and creation of E2E Test Cases. Evaluate models scalability and latency by running suites of metrics under different RPS and creating and automating the test cases for individual models, ensuring a smooth rollout of the models. Qualifications: Bachelor's or master's degree in computer science, Engineering, Mathematics, or a related field. Minimum of 3-8 years of experience in machine learning, with a proven track record of building, monitoring, large scale ML models. Strong problem-solving skills and understanding of recommendation system. Strong programming skills in languages like Python, Scala, and Java. Hands on experience in Databricks, mlFlow, Seldon, Kubeflow, Jenkins, Tecton. Familiarity with custom machine learning platforms, feature store and monitoring ML Models. Expertise in recommendation algorithms. Experience with software engineering principals and use of cloud services like AWS . Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. Mandatory Skills: Databricks, ML flow, Seldon, Kubeflow, ML Load Testing, AWS Services, Tecton, Jenkins, Java/Python/Scala, Evaluate Scalability & latency of Models.

Your Role Design and implement scalable, cloud-native MLOps infrastructure to support the entire ML lifecycle, from experimentation to production deployment Help develop IAC scripts for required Infra provisioning Establish best practices for ML infrastructure monitoring, logging, and observability Collaborate with MLOPS pipeline developers to understand their workflows and optimize infrastructure accordingly Drive technical decisions around tooling selection and infrastructure architecture Mentor junior engineers and provide technical leadership to the MLOps team Your Background Key ingredients for succeeding in this role are your: 7+ years of software engineering experience, with at least 3 years focused on ML/AI infrastructure Strong experience with cloud platforms (AWS/GCP/Azure) and container orchestration (Kubernetes) Expertise in infrastructure-as-code using tools like Terraform, CloudFormation Proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, etc.) Deep understanding of ML workflows, including training, experimentation, and deployment Experience with MLOps tools (MLflow, Kubeflow, Seldon, etc.) and CI/CD platforms (Jenkins, GitLab CI, etc.) Strong background in distributed systems and scalable architecture design Experience with monitoring and observability tools