33 Embeddings Jobs - Page 2

The company Raven, a YC-backed startup (S22), is dedicated to developing AI assistants for manufacturing plants by leveraging decades of manufacturing expertise combined with AI technology to address operational challenges. With strong support from top VCs, the team in Bangalore focuses on enhancing industrial operations to promote safety, intelligence, and efficiency within challenging work environments. As a part of the team, your key responsibilities will revolve around the core technical aspect of constructing AI-native applications and agents tailored for manufacturing workflows. Your tasks will include: - Building Python/Go backend services that seamlessly integrate with AI systems. - Enhancing multi-modal pipeline infrastructure to manage P&IDs, SOPs, sensor data, and technical documents effectively. - Developing agent memory systems utilizing knowledge graphs and event timelines. - Designing AI orchestration layers to facilitate decision-making workflows based on structured/unstructured plant data. - Rapid prototyping of new AI workflows and deploying them in real-world plant settings. The ideal candidate for this role should possess: - 2+ years of experience in constructing production systems. - Proficiency in Python/Go programming, with familiarity in LLMs, embeddings, and vector storage. - A keen interest in problem-solving and a proactive approach towards finding solutions. - A deep commitment to problem ownership throughout the entire process, from exploring potential solutions to implementing functional systems in production. - Comfort in handling ambiguity, adapting quickly, and delivering tangible value. A bonus would be past experience working in a startup environment and a willingness to take on various responsibilities. Joining Raven means being part of a team dedicated to developing fundamental systems that empower plant teams to make quicker and safer decisions. The role offers: - Impact: As one of the initial hires, you will play a pivotal role in shaping the product, culture, and trajectory of the company. - Ownership: You will be granted 0.1-1% equity, emphasizing the importance of feeling a sense of ownership in the company. - Focus: An opportunity to tackle significant, complex problems that directly influence real-world outcomes related to safety and efficiency. The company values the collaboration and fast iteration that come with working together in person, particularly at this stage of growth. The emphasis on teamwork and proximity aims to enhance collaboration and accelerate progress towards shared goals.,

As a Data Scientist specializing in Generative AI & ML Engineering, your primary responsibility will be to research and develop AI algorithms and models. You will be tasked with analyzing data, constructing predictive models, and employing machine learning techniques to address intricate problems. Your proficiency should encompass a range of skills including proficiency in languages/frameworks such as Fast API and Azure UI Search API (React), as well as expertise in databases and ETL tools like Cosmos DB and Data Factory Data Bricks. In addition, you should have a strong command of Python and R, familiarity with Azure Cloud Basics and Gitlab Pipeline, and experience in deploying AI solutions end-to-end. In addition to your proficient skills, you are expected to possess expert-level knowledge in areas such as Azure Open AI, Open AI GPT Family of models, and Azure Storage Account. Your expertise should extend to machine learning algorithms, deep learning frameworks like TensorFlow and PyTorch, and a solid foundation in mathematics including linear algebra, calculus, probability, and statistics. Furthermore, your role will require proficiency in data analysis tools such as Pandas, NumPy, and SQL, as well as strong statistical and probabilistic modeling skills. Experience with data visualization tools like Matplotlib, Seaborn, and Tableau, along with knowledge of big data technologies like Spark and Hive, will be essential for success in this position. Overall, your experience in AI-driven analytics and decision-making systems, coupled with your ability to develop and deploy AI frameworks and models, will be critical in delivering effective solutions to complex challenges.,

We are looking for exceptional individuals to join our team at ScalePad as Head of AI Engineering. ScalePad is a prominent software-as-a-service (SaaS) company operating globally to provide Managed Service Providers (MSPs) with the tools and support needed to enhance client value in the ever-evolving IT landscape. As a member of our tech-management team, you will lead AI development initiatives, shape our AI strategy, and guide teams in creating impactful AI applications. This hands-on leadership role involves mentoring teams, improving developer productivity, and ensuring best practices in AI development, software engineering, and system design. Your responsibilities will include designing state-of-the-art AI applications, leveraging advanced techniques such as Machine Learning (ML), Large Language Models (LLMs), Graph Neural Networks (GNNs), and Retrieval-Augmented Generation (RAG). You will also focus on fostering an environment of responsible AI practices, governance, and ethics, advocating for AI-first product thinking, and collaborating with various teams to align AI solutions with business objectives. To excel in this role, you should possess strong technical expertise in AI, ML, software architecture principles, and have a proven track record of integrating AI advancements into engineering execution. Additionally, experience in AI governance, ethics, and managing globally distributed teams will be essential. We are seeking a curious, hands-on leader who is passionate about developing talent, driving innovation, and ensuring AI excellence within our organization. Joining our team at ScalePad will offer you the opportunity to lead the evolution of AI-driven products, work with cutting-edge technologies, and make a global impact by influencing AI-powered decision-making at an enterprise level. As a Rocketeer, you will enjoy ownership through our Employee Stock Option Plan (ESOP), benefit from annual training and development opportunities, and work in a dynamic, entrepreneurial setting that promotes growth and stability. If you are ready to contribute to a culture of innovation, collaboration, and success, we invite you to apply for this role. Please note that only candidates eligible to work in Canada will be considered. At ScalePad, we are committed to fostering Diversity, Equity, Inclusion, and Belonging (DEIB) to create a workplace where every individual's unique experiences and perspectives are valued. Join us in building a stronger, more inclusive future where everyone has the opportunity to thrive and grow.,

We're hiring a passionate Data Scientist / GenAI Engineer to join our AI-first team working on LLMs, RAG pipelines, NLP features, and GenAI use cases like chatbots, recommendation engines, and smart automation.

Hiring a Senior GenAI Engineer with 712 years of experience in Python, Machine Learning, and Large Language Models (LLMs) for a 6-month engagement based in Gurugram. This hands-on role involves building intelligent systems using Langchain and RAG, developing agent workflows, and defining technical roadmaps. The ideal candidate will be proficient in LLM architecture, prompt engineering, vector databases, and cloud platforms (AWS, Azure, GCP). The position demands strong collaboration skills, a system design mindset, and a focus on production-grade AI/ML solutions.

Role: ML Engineer. Exp : 5 Years to 10 Years Location : Hyderabad. Job Overview: Were seeking a ML Engineer / Data Scientist to architect agentic AI solutions and own the full ML lifecycle—from proof-of-concept to production. You’ll operationalize LLMs, build agentic workflows, implement MLOps best practices, and design multi-agent systems for cybersecurity tasks. Key Responsibilities: Operationalize large language models and agentic workflows (LangChain, LangGraph, LlamaIndex) to automate security decision-making and threat response. Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response. Build proof-of-concept GenAI solutions and evolve them into production-ready components on AWS (Bedrock, SageMaker, Lambda, EKS/ECS) using reusable best practices. Implement CI/CD pipelines for model training, validation, and deployment with GitHub Actions, Jenkins, and AWS CodePipeline. Manage model versioning with MLflow and DVC, set up automated testing, rollback procedures, and retraining workflows. Automate cloud infrastructure provisioning with Terraform and develop REST APIs and microservices containerized with Docker and Kubernetes. Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyze performance and optimize costs and SLA compliance. Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows. Qualifications: Bachelor’s or master’s in computer science, Data Science, AI or related quantitative discipline. 4+ years of software development experience, including 3+ years building and deploying LLM-based/agentic AI architectures. In-depth knowledge of generative AI fundamentals (LLMs, embeddings, vector databases, prompt engineering, RAG). Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI or equivalent agentic frameworks. Strong proficiency in Python and production-grade coding for data pipelines and AI workflows. Deep MLOps knowledge: CI/CD for ML, model monitoring, automated retraining, and production-quality best practices. Extensive AWS experience with Bedrock, SageMaker, Lambda, EKS/ECS, S3 (Athena, Glue, Snowflake preferred). Infrastructure as Code skills with Terraform. Experience building REST APIs, microservices, and containerization with Docker and Kubernetes. Solid data science fundamentals: feature engineering, model evaluation, data ingestion. Understanding of cybersecurity principles, SIEM data, and incident response. Excellent communication skills for both technical and non-technical audiences. Preferred Qualifications: AWS certifications (Solutions Architect, Developer Associate). Nice to have Experience with Model Context Protocol (MCP) and RAG integrations. Nice to have Experience in Crew.AI Familiarity with workflow orchestration tools (Apache Airflow). Experience with time series analysis, anomaly detection, and machine learning.

Proven experience in developing and implementing Generative AI models and algorithms, with a strong understanding of deep learning fundamentals. Experience with training and fine-tuning Generative models on high-performance computing infrastructure. Experience in working with Vector DB and Embedding Proficiency in programming languages such as Python, NLP, TensorFlow, PyTorch, or similar frameworks for building and deploying AI models. Strong analytical and problem-solving and collaboration skills A passion for exploring new ideas, pushing the boundaries of AI technology, and making a meaningful impact through your work.

Essential Skills: Machine Learning & Deep Learning: Solid understanding of ML concepts and hands-on experience with deep learning (especially neural networks and Transformers). Python Programming: Strong Python coding skills with familiarity in frameworks like TensorFlow, PyTorch, or Keras. Natural Language Processing (NLP): Experience with tokenization, embeddings, and language model fine-tuning. Data Engineering: Ability to clean, process, and manage large datasets with a focus on data quality. Math & Statistics: Good knowledge of linear algebra, calculus, probability, and statistics. Preferred Additional Skills: Model Deployment (MLOps): Exposure to deploying models using Docker, APIs, CI/CD, and tools like MLflow or Hugging Face Hub. RAG & LLM Integration: Hands-on with FAISS, LangChain, embedding models, and large language models. Prompt Engineering: Skilled in designing prompts and evaluating AI output quality. System Scalability: Understanding of GPU optimization and AI system performance.