531 Mlops Jobs - Page 22

Immediate opening for "AI Architect (Generative AI)" Location : Remote Experience :- 15 to 23 yrs Quantaleap INC. is seeking a highly experienced AI Architect with deep expertise in Generative AI to drive innovative solution design and architecture for enterprise use cases. This role requires a strategic thinker with a strong foundation in AI/ML technologies and a proven ability to lead high-impact AI initiatives across diverse domains. Key Responsibilities: Architect end-to-end AI solutions leveraging Generative AI technologies (LLMs, diffusion models, transformers, etc.). Evaluate and recommend cutting-edge tools, frameworks, and models suitable for enterprise-grade deployments. Collaborate with cross-functional teams for RFP responses, technical proposals, and client presentations. Lead the development of scalable AI solutions from POC to production across multiple industry verticals. Guide technical teams on best practices in AI model development, fine-tuning, and deployment. Must-Have Skills: 15+ years of overall experience with a strong focus on AI/ML architecture In-depth knowledge of Generative AI technologies, including LLMs (GPT, BERT), GANs, and foundation models Experience in deploying AI models in cloud-native and on-prem environments Strong understanding of MLOps, data pipelines, and model lifecycle management Proven track record in presales, RFP responses, and enterprise solutioning Excellent stakeholder management and client-facing communication skills Interested can share their updated resume to anitha.mudaliyar@quantaleap.com

Role Definition: Data Scientists focus on researching and developing AI algorithms and models. They analyse data, build predictive models, and apply machine learning techniques to solve complex problems. Skills: • Proficient: Languages/Framework: Fast API, Azure UI Search API (React) o Databases and ETL: Cosmos DB (API for MongoDB), Data Factory Data Bricks o Proficiency in Python and R o Cloud: Azure Cloud Basics (Azure DevOps) o Gitlab: Gitlab Pipeline o Ansible and REX: Rex Deployment o Data Science: Prompt Engineering + Modern Testing o Data mining and cleaning o ML (Supervised/unsupervised learning) o NLP techniques, knowledge of Deep Learning techniques include RNN, transformers o End-to-end AI solution delivery o AI integration and deployment o AI frameworks (PyTorch) o MLOps frameworks o Model deployment processes o Data pipeline monitoring Expert: (in addition to proficient skills) o Languages/Framework: Azure Open AI o Data Science: Open AI GPT Family of models 4o/4/3, Embeddings + Vector Search o Databases and ETL: Azure Storage Account o Expertise in machine learning algorithms (supervised, unsupervised, reinforcement learning) o Proficiency in deep learning frameworks (TensorFlow, PyTorch) o Strong mathematical foundation (linear algebra, calculus, probability, statistics) o Research methodology and experimental design o Proficiency in data analysis tools (Pandas, NumPy, SQL) o Strong statistical and probabilistic modelling skills o Data visualization skills (Matplotlib, Seaborn, Tableau) o Knowledge of big data technologies (Spark, Hive) o Experience with AI-driven analytics and decision-making systems

Job Summary: We are seeking an experienced and forward-thinking AI Subject Matter Expert (SME) to lead the design, development, and integration of custom and commercial AI solutions. The ideal candidate will bring deep technical expertise, a passion for innovation, and a strategic mindset to drive impactful AI initiatives across the organization. Key Responsibilities: Custom AI Development: Design, build, and deploy tailored AI models and solutions that address specific business challenges and objectives. Commercial AI Integration: Evaluate, recommend, and implement commercial AI platforms and tools to enhance operational efficiency and business outcomes. Research & Innovation: Stay abreast of emerging AI technologies, trends, and research; identify opportunities to innovate and apply cutting-edge techniques. Advanced Data Analysis: Analyze complex datasets to uncover patterns, support model development, and inform decision-making. Cross-functional Collaboration: Partner with data scientists, software engineers, analysts, and business stakeholders to ensure seamless integration of AI solutions. Documentation & Knowledge Sharing: Create thorough documentation of models, algorithms, and development processes; contribute to organizational knowledge bases. Training & Support: Mentor team members and provide training sessions for stakeholders to build AI literacy and adoption across teams. Qualifications Education: Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: 6-9 years of hands-on experience in AI solution development and implementation, with a strong portfolio of successful projects . Technical Proficiency: Expertise in AI/ML frameworks such as TensorFlow, PyTorch, Keras. Strong programming skills in Python (preferred), R, and experience with data tools such as SQL and Pandas. Familiarity with deploying models in production environments . Analytical & Problem-Solving Skills: Ability to interpret complex data, design intelligent systems, and develop actionable insights . Communication: Strong written and verbal communication skills, capable of translating complex concepts for non-technical audiences . Project Management: Demonstrated ability to lead AI projects end-to-end from planning to deployment and performance monitoring . Adaptability: Comfortable working in a dynamic, fast-paced environment with evolving business needs.

Role & responsibilities Develop, improve, and expand deployment pipeline infrastructure and services Build and deploy hyperscale machine learning pipelines into production Works closely with the developer teams to provide a self-service test integration, test coverage and quality Collaborate with the Security Engineering and Architecture teams to ensure code meets security requirements and is deployed in alignment with standards and governance processes Debug and resolve the issues developers encounter when attempting to use cloud services, with an eye toward continuously improving developer experience Execute against a vision of continuous integration and delivery that empowers developers Preferred candidate profile Building Infrastructure-as-code for Public Cloud using Terraform Experience in a Dev Ops engineering or equivalent role. Experience developing, enhancing, and maintaining CI/CD automation and configuration management using tools such as Jenkins, Snyk, and GitHub Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Optuna, Mlflow or other similar MLOps tools. Experience with operationalizing and migrating ML models into production at scale Experience developing large scale model inference solutions using a parallel execution framework using Spark, EMR, or Databricks Experience developing complex orchestration and MLOps pipelines stitching together large volumes of data for training and scoring Proficiency with Apache Spark, EMR/DataProc and Cloud based tools (Snowflake, Redshift, EMR, Glue, Step Functions, Lambda, Step functions, AWS Batch, or similar etc.) Experience with ML libraries like H20, scikit learn and deep learning frameworks (PyTorch, TensorFlow, etc.) Familiarity with large scale production systems and technologies; for example load balancing, monitoring, distributed systems, and configuration management Development skills with at least one of the following programming languages: Python (preferred), Node.js, Bash. Knowledge of testing automation and code scanning tools Knowledge and experience with running distributed systems in public cloud environments such as AWS, Azure, and GCP. Knowledge of container orchestration platforms such as Kubernetes Knowledge of CDNs, DNS, web servers, and other supporting Internet services Source code management tools using Git. Experienced with standard Gitflow workflow Familiar with best practices for infrastructure security, reliability, and fault tolerance Passionate about automating and improving processes

Role & responsibilities Urgent Hiring for one of the reputed MNC Immediate joiners Only Females Bang / Pune / Hyd Exp - 4 - 12 Years Job Description: Senior Data Scientist - Multi-Agent AI Systems About the Role We are seeking an exceptional Data Scientist with specialized expertise in developing multi-agent AI systems. In this role, you will design, implement, and optimize complex AI ecosystems where multiple intelligent agents collaborate to solve sophisticated problems. You will leverage your deep understanding of generative AI, retrieval-augmented generation (RAG), and prompt engineering to create cutting-edge solutions that push the boundaries of artificial intelligence. Key Responsibilities Design and develop generative AI-based multi-agent systems that can collaborate, communicate, and coordinate to achieve complex objectives Architect and implement RAG-based chatbot solutions that effectively leverage knowledge bases and external data sources Create sophisticated prompt engineering strategies to optimize AI agent behavior and inter-agent communication Build, train, and fine-tune generative AI models for various applications within multi-agent systems Develop robust evaluation frameworks to measure and improve multi-agent system performance Implement efficient knowledge sharing mechanisms between AI agents Write clean, efficient, and well-documented Python code for production-ready AI systems Collaborate with cross-functional teams to integrate multi-agent systems into broader product ecosystems Stay at the forefront of AI research and incorporate state-of-the-art techniques into our solutions Required Qualifications Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field 4+ years of professional experience in data science or machine learning engineering Extensive experience with Python programming and related data science/ML libraries Demonstrated expertise in developing and deploying generative AI models (e.g., LLMs, diffusion models) Proven experience building RAG-based systems and implementing vector databases Strong background in prompt engineering for large language models Experience designing and implementing generative AI-based multi-agent architectures Excellent problem-solving skills and ability to optimize complex AI systems Preferred candidate profile Experience with LangChain, AutoGPT, CrewAI, or similar frameworks for building agent-based systems Familiarity with orchestration tools for managing complex AI workflows Knowledge of agent communication protocols and collaborative problem-solving frameworks Experience with distributed systems and cloud computing platforms (AWS, GCP, Azure) Contributions to open-source AI projects or research publications in relevant fields Experience with knowledge graphs and semantic reasoning systems Familiarity with MLOps practices and deployment of AI systems at scale

Key Responsibilities : Design, develop, and deploy machine learning models and AI solutions that address complex business challenges, particularly within the AEC domain. Build scalable, production-grade AI/ML pipelines, including data preprocessing, feature engineering, model training, validation, deployment, and monitoring. Collaborate with Data Engineers, Data Scientists, and other stakeholders to design and implement end-to-end AI/ML solutions. Ensure that deployed models are reliable, maintainable, and scalable while optimizing their performance in production environments. Develop APIs or microservices for integrating AI/ML models into business solutions. Optimize AI/ML workflows by implementing MLOps practices, including CI/CD pipelines, model retraining, and version control. Apply state-of-the-art algorithms and techniques such as deep learning, NLP, computer vision, and time-series analysis for domain-specific use cases. Maintain adherence to data privacy, security, and ethical AI standards throughout the lifecycle of AI/ML models. Stay abreast of the latest developments and advancements, including new and emerging technologies & best practices and new tools & software applications and how they could impact CDM Smith. Assist with the development of documentation, standards, best practices, and workflows for data technology hardware/software in use across the business. Performs other duties as required Skills and Abilities: Strong expertise in building and deploying machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn. Hands-on experience with cloud-based AI/ML services, particularly in Microsoft Azure and Databricks. Proficiency in programming languages such as Python or R, with a strong understanding of libraries and tools for data science and machine learning. Experience with MLOps practices, including automated pipelines, model versioning, monitoring, and lifecycle management. Familiarity with distributed computing frameworks like Apache Spark and scalable AI/ML training techniques. Expertise in domain-specific AI techniques such as Natural Language Processing (NLP), computer vision, and predictive analytics. Knowledge of data privacy, security, and ethical AI principles, ensuring compliance with relevant standards. Excellent problem-solving and critical thinking skills to identify and address technical challenges effectively. Excellent interpersonal and presentation skills to build strategic relationships with colleagues, stakeholders, and partners. Strong critical thinking skills to generate innovative solutions and improve business processes. Ability to effectively communicate complex technical concepts to both technical and nontechnical audiences. Detail oriented with the ability to assist with executing highly complex or specialized projects. Minimum Qualifications: Bachelors degree. 6 years of related experience. Equivalent additional related experience will be considered in lieu of a degree.