515 Mlops Jobs - Page 10

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. Your role will involve a solid understanding and hands-on skills for GenAI application deployment, which includes development and engineering tasks. This will include data ingestion, selecting suitable LLMs, implementing simple and advanced RAG, setting up guardrails, prompt engineering for optimization, ensuring traceability and security, evaluating LLMs, enabling observability, and deploying at scale on the cloud or on-premise. It is crucial that candidates also showcase knowledge on agentic AI frameworks, with a preference for those having a strong background in ML with engineering skills for the LLMOps role. The ideal candidate should possess 3 - 5 years of experience in working on ML projects, encompassing tasks such as business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, and optionally AgentOps is essential. Prior experience working with both proprietary and open-source large language models, fine-tuning LLMs, creating distilled models from hosted LLMs, building data pipelines for model training, and tuning model performance, RAG, guardrails, prompt engineering, evaluation, and observability is required. Moreover, candidates should have experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, deploying AI services on at least one cloud platform (AWS/GCP/Azure), creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and optionally developing lightweight UI using streamlit or chainlit. Desired experience with open-source tools for ML development, deployment, observability, and integration, as well as a background in DevOps and MLOps, will be advantageous. Proficiency in collaborative code versioning tools such as GitHub/GitLab, along with strong communication and presentation skills, is essential. A B.E/B.Tech/M.Tech in Computer Science or a related technical degree or equivalent qualification is required. If you are someone who enjoys challenging growth opportunities and thrives in a dynamic environment working alongside enthusiastic over-achievers, this role might be the perfect fit for you.,

The company is seeking a Gen AI / AI Engineer with at least 2 years of experience in the field of AI/ML solutions, specifically focusing on Generative AI and RAG (Retrieval-Augmented Generation) architectures. The role involves working on cutting-edge models, fine-tuning, and integrating systems to develop scalable AI products. Responsibilities include fine-tuning and optimizing Large Language Models (LLMs) and generative models for text and image, designing and implementing RAG pipelines with knowledge bases, developing APIs, microservices, and scalable AI pipelines, collaborating with MLOps teams for production deployment, and staying updated on the latest AI trends to apply innovative techniques. The ideal candidate should possess skills in Python, PyTorch/TensorFlow, Generative AI (OpenAI, Anthropic, LLaMA), RAG architecture, Natural Language Processing (NLP), Computer Vision, API development, MLOps, and have experience working with Cloud Platforms such as AWS/Azure/GCP. Qualifications include a Bachelors degree in Computer Science or a related field, at least 2 years of experience in AI/ML with a focus on GenAI & RAG, and hands-on experience with production-grade model deployment. If you are enthusiastic about contributing to the development of next-generation AI solutions, we encourage you to apply for this exciting opportunity now!,

About Position: We are conducting an in-person hiring drive for the position of Mlops / Data Science in Pune & Bengaluru on 19th July 2025.Interview Location is mentioned below: Pune Persistent Systems, Veda Complex, Rigveda-Yajurveda-Samaveda-Atharvaveda Plot No. 39, Phase I, Rajiv Gandhi Information Technology Park, Hinjawadi, Pune, 411057 Bangalore - Persistent Systems, The Cube at Karle Town Center Rd, DadaMastan Layout, Manayata Tech Park, Nagavara, Bengaluru, Karnataka 560024 We are looking for an experienced and talented Data Science to join our growing data competency team. The ideal candidate will have a strong background in working with GEN AI , ML ,LangChain, LangGraph, Mlops Architecture Strategy, Prompt engineering. You will work closely with our data analysts, engineers, and business teams to ensure optimal performance, scalability, and availability of our data pipelines and analytics. Role: Mlops, Data Science Job Location: All PSL Location Experience: 5+ Years Job Type: Full Time Employment What You'll Do: Design, build, and manage scalable ML model deployment pipelines (CI/CD for ML). Automate model training, validation, monitoring, and retraining workflows. Implement model governance, versioning, and reproducibility best practices. Collaborate with data scientists, engineers, and product teams to operationalize ML solutions. Ensure robust monitoring and performance tuning of deployed models Expertise You'll Bring: Strong experience with MLOps tools & frameworks (MLflow, Kubeflow, SageMaker, Vertex AI, etc.). Proficient in containerization (Docker, Kubernetes). Good knowledge of cloud platforms (AWS, Azure, or GCP). Expertise in Python and familiarity with ML libraries (TensorFlow, PyTorch, scikit-learn). Solid understanding of CI/CD, infrastructure as code, and automation tools. 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 Inclusive Environment: Persistent Ltd. is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds. We offer hybrid work options and flexible working hours to accommodate various needs and preferences. Our office is equipped with accessible facilities, including adjustable workstations, ergonomic chairs, and assistive technologies to support employees with physical disabilities. If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment. We are committed to creating an inclusive environment where all employees can thrive. Our company fosters a values-driven and people-centric work environment that enables our employees to: Accelerate growth, both professionally and personally Impact the world in powerful, positive ways, using the latest technologies Enjoy collaborative innovation, with diversity and work-life wellbeing at the core Unlock global opportunities to work and learn with the industry's best Let's unleash your full potential at Persistent "Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind."

We are looking for a proactive and detail-oriented AI OPS Engineer to support the deployment, monitoring, and maintenance of AI/ML models in production. This role will focus on MLOps practices including model versioning, CI/CD, observability, and performance optimization in cloud and hybrid environments. Role & responsibilities Build and manage CI/CD pipelines for ML models using platforms like MLflow, Kubeflow, or SageMaker. Monitor model performance and health using observability tools and dashboards. Ensure automated retraining, version control, rollback strategies, and audit logging for production models. Support deployment of LLMs, RAG pipelines, and agentic AI systems in scalable, containerized environments. Collaborate with AI Developers and Architects to ensure reliable and secure integration of models into enterprise systems. Troubleshoot runtime issues, latency, and accuracy drift in model predictions and APIs. Contribute to infrastructure automation using Terraform, Docker, Kubernetes, or similar technologies. Preferred candidate profile Experience in DevOps, MLOps, or platform engineering roles with exposure to AI/ML workflows. Hands-on experience with deployment tools like Jenkins, Argo, GitHub Actions, or Azure DevOps. Strong scripting skills (Python, Bash) and familiarity with cloud environments (AWS, Azure, GCP). Understanding of containerization, service orchestration, and monitoring tools (Prometheus, Grafana, ELK).

Key Responsibilities: Lead AI/ML solution development using Python, including API integration and data processing tasks. Conduct research and feasibility analysis for GenAI use cases relevant to the customers business. Review offshore deliverables to ensure adherence to best practices and quality standards before client submission. Collaborate with cross-functional teams to align technical solutions with business goals. Confidently articulate AI/ML concepts and practices to both technical and non-technical stakeholders. Preferred Skills: Strong hands-on experience in Python programming. Exposure to ML Ops practices and deployment workflows. Ability to mentor junior team members and guide solution design. Excellent communication and stakeholder management skills.

Role & responsibilities Design and implement AI agent workflows. Develop end-to-end intelligent pipelines and multi-agent systems (e.g., LangGraph/LangChain workflows) that coordinate multiple LLM-powered agents to solve complex tasks. Create graph-based or state-machine architectures for AI agents, chaining prompts and tools as needed. Build and fine-tune generative models. Develop, train, and fine-tune advanced generative models (transformers, diffusion models, VAEs, GANs, etc.) on domain-specific data. Deploy and optimize foundation models (such as GPT, LLaMA, Mistral) in production, adapting them to our use cases through prompt engineering and supervised fine-tuning. Develop data pipelines. Build robust data collection, preprocessing, and synthetic data generation pipelines to feed training and inference workflows. Implement data cleansing, annotation, and augmentation processes to ensure high-quality inputs for model training and evaluation. Implement LLM-based agents and automation. Integrate generative AI agents (e.g., chatbots, AI copilots, content generators) into business processes to automate data processing and decision-making tasks. Use Retrieval-Augmented Generation (RAG) pipelines and external knowledge sources to enhance agent capabilities. Leverage multimodal inputs when applicable. Optimize performance and safety. Continuously evaluate and improve model/system performance. Use GenAI-specific benchmarks and metrics (e.g., BLEU, ROUGE, TruthfulQA) to assess results, and iterate to optimize accuracy, latency, and resource efficiency. Implement safeguards and monitoring to mitigate issues like bias, hallucination, or inappropriate outputs. Collaborate and document. Work closely with product managers, engineers, and other stakeholders to gather requirements and integrate AI solutions into production systems. Document data workflows, model architectures, and experimentation results. Maintain code and tooling (prompt libraries, model registries) to ensure reproducibility and knowledge sharing. Education: Bachelors or Master’s degree in Computer Science, Data Science, Artificial Intelligence. Programming proficiency: Expert-level skills in Python experience in machine learning and deep learning frameworks (PyTorch, TensorFlow). Generative model expertise: Demonstrated ability to build, fine-tune, and deploy large-scale generative models Familiarity with transformer architectures and generative techniques (LLMs, diffusion models, GANs) Experience working with model repositories and fine-tuning frameworks (Hugging Face, etc.). LLM and agent frameworks: Strong understanding of LLM-based systems and agent-oriented AI patterns. Experience with frameworks like LangGraph/LangChain or similar multi-agent platforms. AI integration and MLOps: Experience integrating AI components with existing systems via APIs and services. . Proficiency in retrieval-augmented generation (RAG) setups, vector databases,Familiarity with machine learning deployment and MLOps tools (Docker, Kubernetes, MLflow, KServe, etc.) Familiarity with GenAI tools: Hands-on experience with state-of-the-art GenAI models and APIs (OpenAI GPT, Anthropic, Claude, etc.) and with popular libraries (Hugging Face Transformers, LangChain, etc.). Awareness of the current GenAI tooling ecosystem and best practices.

AI/ML Engineer (Computer Vision) Job Location Remote OR Jaipur or Hyderabad preferred (Hybrid) Experience Range 5-6 Y Timings 3 hours overlap with EST Timing Job Description: Enovate AI seeks an AI/ML Engineer with expertise in computer vision to develop a privacy- compliant facial recognition module for our drive-through ordering software. You will build models to generate temporary facial vectors for customer identification, ensuring seamless personalization. Responsibilities: Develop facial recognition models using frameworks like FaceNet or ArcFace. Generate temporary facial vectors for session-based customer identification. Ensure compliance with privacy standards by avoiding permanent biometric storage. Optimize models for robustness in varied lighting conditions. Integrate with CRM modules to retrieve customer profiles based on recognition results. Collaborate with backend engineers to process camera feeds via cloud APIs. Qualifications: • Masters or PhD in Computer Science, AI, or related field (or equivalent experience). 3+ years of experience in computer vision or facial recognition. Proficiency in Python, OpenCV, and ML frameworks (TensorFlow, PyTorch). Experience with facial embedding techniques and similarity matching. Strong understanding of privacy-compliant biometric processing. Ability to work with cross-functional teams on software integration. Preferred Skills: Experience with cloud-based computer vision APIs (e.g., AWS Rekognition). If interested, please share your resume on ekta.srivastava@programmers.io

MLOPs + DevOps + Sagemaker + AWS Services is must. Profiles will not fit without DevOps background. Need screening notes/experience on below : Proven hands-on experience with AWS services such as EC2, Lambda, IAM, S3, CloudFormation, ECS, EKS, RDS and Route53. Knowledge of SNS, SES, Amazon SageMaker and Amazon Bedrock services Proficiency with source control systems, especially Git. Working knowledge of Terraform Experience with Docker containerization and an understanding of CI/CD processes are a plus DevOps, Sagemaker, Aws Services, MLOPs , EC2, Lambda, IAM, S3, CloudFormation, ECS, EKS, RDSE,TerraformS, Amazon

Job Title : Data Scientist - AI Integration & Orchestration. Job Summary : We are seeking a skilled Data Scientist with expertise in AI orchestration and embedded systems to support a sprint-based Agile implementation focused on integrating generative AI capabilities into enterprise platforms such as Slack, Looker, and Confluence. The ideal candidate will have hands-on experience with Gemini and a strong understanding of prompt engineering, vector databases, and orchestration infrastructure. Key Responsibilities : - Develop and deploy Slack-based AI assistants leveraging Gemini models. - Design and implement prompt templates tailored to enterprise data use cases (Looker and Confluence). - Establish and manage an embedding pipeline for Confluence documentation. - Build and maintain orchestration logic for prompt execution and data retrieval. - Set up API authentication and role-based access controls for integrated systems. - Connect and validate vector store operations (e.g., Pinecone, Weaviate, or Snowflake vector extension). - Contribute to documentation, internal walkthroughs, and user acceptance testing planning. - Participate in Agile ceremonies including daily standups and sprint demos. Required Qualifications : - Proven experience with Gemini and large language model deployment in production environments. - Proficiency in Python, orchestration tools, and prompt engineering techniques. - Familiarity with vector database technologies and embedding workflows. - Experience integrating APIs for data platforms such as Looker and Confluence. - Strong understanding of access control frameworks and enterprise-grade authentication. - Demonstrated success in Agile, sprint-based project environments. Preferred Qualifications : - Experience working with Slack app development and deployment. - Background in MLOps, LLMOps, or AI system orchestration at scale. - Excellent communication skills and ability to work in cross-functional teams.

Siemens Energy is looking for a talented Senior MLOps Engineer to join the Digital Core team and contribute to shaping the data architecture and strategy within the organization. In this role, you will collaborate closely with stakeholders to drive developments in the Machine Learning environment. Your responsibilities will include partnering with business stakeholders, defining backlog priorities, consulting on AI ML solutions, supporting test automation, building CI CD pipelines, and working on PoCs/MVPs using various hyperscale offerings. The Siemens Energy Data Analytics & AI team is at the forefront of driving the energy transition and addressing environmental challenges. As part of this team, you will have the opportunity to work on innovative projects that reimagine the future and contribute to a sustainable world. Your impact will involve onboarding new AI ML use cases in AWS/Google Cloud Platform, defining MLOps architecture, deploying models, working with AI ML services like AWS Sagemaker and GCP AutoML, developing PoCs/MVPs, implementing CI CD pipelines, writing Infrastructure as code using AWS CDK scripts, providing consultancy on AI ML solutions, and supporting test automation and code deployment. To excel in this role, you should have a Bachelor's degree in Computer Science, Mathematics, Engineering, Physics, or related field (a Master's degree is a plus), AWS or GCP Certifications in ML AI, around 10 years of hands-on experience in ML/AI development and operations, expertise in ML Life Cycle and MLOps, proficiency in Python coding and Linux administration, experience with CI CD pipelines, and familiarity with JIRA, Confluence, and Agile delivery model. Additionally, you should possess excellent interpersonal, communication, and collaborative skills, be result-driven, and fluent in both spoken and written English. As part of the Data Platforms and Services organization at Siemens Energy, you will contribute to the company's mission of becoming a data-driven organization and supporting customers in transitioning to a more sustainable world through innovative technologies. Siemens Energy is a global energy technology company committed to providing sustainable, reliable, and affordable energy solutions while protecting the climate. With a diverse team of over 92,000 employees across 90+ countries, Siemens Energy is focused on decarbonization, new technologies, and energy transformation. Siemens Energy values diversity and inclusion, celebrating the unique contributions of individuals from over 130 nationalities. The company is dedicated to providing equal opportunities and encourages applications from individuals with disabilities. Join Siemens Energy in energizing society and driving positive change in the energy sector.,

You will be joining InnovaESI as a ML Engineer/Data Scientist in Bengaluru, in a full-time hybrid role. Your responsibilities will include working on pattern recognition, neural networks, algorithms, and utilizing statistical methods to deliver comprehensive data solutions. While your primary location will be in Bengaluru, there is also flexibility for remote work. To excel in this role, you should have a strong background in Computer Science and Algorithms, proficiency in Pattern Recognition and Neural Networks, knowledge of Statistics and its application in data analysis, as well as experience working with complex algorithms and data models. Collaborating effectively in cross-functional teams and possessing advanced degrees in Computer Science, Data Science, or related fields are also essential qualifications. Additionally, a deep understanding of machine learning and NLP, coupled with commercial experience in building and deploying LLM solutions, will be beneficial. You should have 4-8 years of relevant experience, exceptional coding abilities (preferably in Python), and familiarity with ML tools and libraries like TensorFlow, PyTorch, pandas, scikit-learn, among others. Furthermore, a strong understanding of MLOps best practices with experience in deploying LLM applications to production at scale is required. Being a problem solver and self-starter is key; you should enjoy tackling challenging problems independently and within a collaborative team environment. Strong communication skills to explain complex technical concepts to non-technical stakeholders are also crucial. If you are passionate about leveraging your expertise in ML and data science to drive digital transformation, we encourage you to share your resume with us at jaya.sharma@innovaesi.com.,

At OP, we are a people-first, high-touch organization committed to delivering cutting-edge AI solutions with integrity and passion. We are looking for a Senior AI Developer with expertise in AI model development, Python, AWS, and scalable tool-building. In this role, you will be responsible for designing and implementing AI-driven solutions, developing AI-powered tools and frameworks, and integrating them into enterprise environments, including mainframe systems. As a Senior AI Developer at OP, your key responsibilities will include developing and deploying AI models using Python and AWS for enterprise applications, building scalable AI-powered tools, designing and optimizing machine learning pipelines, implementing NLP and GenAI models, developing and integrating RAG systems for enterprise knowledge retrieval, maintaining AI frameworks and APIs, architecting cloud-based AI solutions using AWS services, writing high-performance Python code for AI applications, and ensuring scalability, security, and performance of AI solutions in production. The required qualifications for this role include 5+ years of experience in AI/ML development with expertise in Python and AWS, a strong background in machine learning and deep learning, experience in LLMs, NLP, and RAG systems, hands-on experience in building and deploying AI models in production, proficiency in cloud-based AI solutions, experience in developing AI-powered tools and frameworks, knowledge of mainframe integration and enterprise AI applications, and strong coding skills with a focus on software development best practices. Preferred qualifications for this role include familiarity with MLOps, CI/CD pipelines, and model monitoring, a background in developing AI-based enterprise tools and automation, and experience with vector databases and AI-powered search technologies. At OP, we offer health insurance, accident insurance, and competitive salaries based on various factors including location, education, qualifications, experience, technical skills, and business needs. In addition to the core responsibilities, you will also be expected to participate in OP monthly team meetings, contribute to technical discussions, peer reviews, and collaborate via the OP-Wiki/Knowledge Base, as well as provide status reports to OP Account Management as requested. OP is a technology consulting and solutions company that offers advisory and managed services, innovative platforms, and staffing solutions across various fields, including AI, cyber security, and enterprise architecture. Our team consists of dynamic, creative thinkers who are passionate about quality work, and as a member of the OP team, you will have access to industry-leading consulting practices, strategies, technologies, training, and education. An ideal OP team member is a technology leader with a proven track record of technical excellence and a strong focus on process and methodology.,

Oraczen is at the forefront of Generative AI, pioneering next-generation AI-driven applications that redefine industry standards. The company is committed to innovation, collaboration, and technical excellence, enabling enterprises to seamlessly integrate artificial intelligence into their operations. The team at Oraczen consists of top-tier AI experts, engineers, and product innovators dedicated to pushing the boundaries of AI/ML applications. The company fosters a culture of continuous learning, inclusion, and excellence to create cutting-edge solutions that transform businesses. As a Product Architect at Oraczen, you will lead the development of scalable, high-performance AI applications. With over 10 years of expertise in Python and AI/ML, you will collaborate with AI researchers, product managers, and software architects to design and implement cutting-edge AI-driven solutions. Your role will involve developing advanced Generative AI models and optimizing machine learning frameworks to solve complex industry challenges. Key Responsibilities: - Architect, design, and develop enterprise-grade AI applications using Python and leading AI/ML frameworks. - Provide technical leadership and mentorship to engineering teams, fostering a culture of excellence and innovation. - Collaborate with cross-functional teams to define AI system architectures for various applications. - Develop and optimize deep learning and machine learning models in areas such as Natural Language Processing, Computer Vision, and Generative AI. - Implement and fine-tune large-scale AI models to ensure efficiency, accuracy, and performance in real-world applications. - Lead code reviews, technical discussions, and AI research initiatives to uphold high coding standards and drive innovation. - Troubleshoot, debug, and optimize AI-driven applications to enhance scalability, reliability, and user experience. - Stay updated on industry trends by researching and integrating cutting-edge AI methodologies and technologies. - Design and implement MLOps pipelines for streamlined AI model deployment, monitoring, and maintenance. - Contribute to patents, research publications, and open-source AI projects to drive thought leadership in the AI industry. Qualifications & Skills: - Bachelor's or master's degree in computer science, Software Engineering, or a related field (PhD preferred). - 10+ years of experience in software development with a strong focus on Python and AI/ML. - Extensive knowledge of Generative AI, Deep Learning, and Machine Learning frameworks such as TensorFlow, PyTorch, Hugging Face, and OpenAI APIs. - Proven expertise in Natural Language Processing, Computer Vision, or Large Language Models. - Hands-on experience in cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). - Strong understanding of scalable AI architectures, distributed computing, and data engineering best practices. - Experience with MLOps tools and techniques for deploying AI models in production environments. - Excellent problem-solving skills and the ability to thrive in a fast-paced, agile development environment. - Strong leadership, mentorship, and technical communication skills to collaborate effectively with engineering teams. Why Join Us - Lead the development of cutting-edge AI-driven applications in an innovative, future-focused company. - Work alongside top-tier AI experts and engineers on groundbreaking projects. - Competitive salary, equity options, and comprehensive benefits package. - A collaborative and inclusive team culture that encourages continuous learning and professional growth. - Access to state-of-the-art AI research, tools, and high-performance computing resources. - Professional development opportunities, including AI conferences, certifications, and leadership training.,

You are an experienced Lead Generative AI Engineer responsible for training, optimizing, scaling, and deploying various generative AI models including large language models, voice/speech foundation models, vision, and multi-modal foundation models using cutting-edge techniques and frameworks. Your role involves architecting and implementing state-of-the-art neural architecture, robust training, and inference infrastructure to efficiently take complex models with billions of parameters to production while optimizing for low latency, high throughput, and cost efficiency. Your key responsibilities include: - Architecting and refining foundation model infrastructure to support optimized AI models deployment focusing on C/C++, CUDA, and kernel-level programming enhancements. - Implementing optimization techniques like quantization, distillation, sparsity, streaming, and caching for model performance enhancements. - Spearheading the development of Vision pipelines to ensure scalable training and inference workflows of billions of parameter foundation models. - Innovating for state-of-the-art architectures involving Panoptic Segmentation, Image Classification, and Image Generation. - Designing, developing, and innovating state-of-the-art large multimodal models. - Executing training and inference processes to minimize latency and maximize throughput utilizing GPU clusters and custom hardware. - Integrating and tailoring frameworks like PyTorch, TensorFlow, DeepSpeed, Lightening, FSDP, and Habana for fast model training and inference. - Enhancing post-deployment mechanisms with exhaustive testing, real-time monitoring, and robustness checks. - Driving continuous improvement initiatives for deployed models with automated pipelines for drift detection and performance degradation. - Leading the charge in model management including version control, reproducibility, and lineage tracking. - Cultivating a culture of high-performance computing and optimization within the AI/ML domain. Qualifications: - Ph.D. with 5+ years or MS with 8+ years of experience in ML Engineering, Data Science, or related fields. - Demonstrated expertise in high-performance computing with proficiency in Python, C/C++, CUDA, and kernel-level programming for AI applications. - Extensive experience in optimizing training and inference for large-scale AI models. - Understanding of Diffusion Models, Variational Autoencoders, Bayesian Modelling, and Reinforcement Learning is beneficial. - Experience in building billions of parameters generative AI foundation models. - Proven success in deploying optimized ML systems on a large scale using cloud infrastructures and GPU resources. - In-depth understanding and hands-on experience with advanced model optimization frameworks and MLOps tools. - Familiarity with contemporary MLOps frameworks and their application in production environments. - Strong grasp of state-of-the-art ML infrastructures, deployment strategies, and optimization methodologies. - Innovative problem-solving skills and collaborative mindset. - Exceptional communication and team collaboration skills.,

As a research scientist at AryaXAI, you will be part of a team that is revolutionizing the field of AI with a focus on building explainable, safe, and aligned systems at scale. Our mission is to empower researchers, engineers, and organizations to leverage AI effectively while prioritizing transparency and safety. Our team is driven by a shared passion for innovation, collaboration, and excellence. In our flat organizational structure, everyone contributes actively to our mission, valuing curiosity, initiative, and outstanding performance. In this role, you will tackle advanced challenges in ML explainability, safety, and alignment. You will have the flexibility to specialize in specific areas within ML/DL and address various problem types related to these challenges. Additionally, you will be involved in creating new techniques around ML Observability & Alignment, collaborating with MLEs and SDEs to implement features, ensuring their quality and stability, and managing technical and product documentation. To excel in this position, you should have a solid academic foundation in machine learning and deep learning concepts. Hands-on experience with deep learning frameworks like Tensorflow and PyTorch is essential. You should enjoy working on diverse DL problems that involve different types of training datasets such as textual, tabular, categorical, and images. Proficiency in deploying code in cloud/on-premise environments, strong fundamentals in MLOps, and prior experience with ML explainability methods like LRP, SHAPE, LIME, IG, CEM are required. Ideally, you should have at least 2 years of hands-on experience in Deep Learning or Machine Learning, along with practical experience in implementing techniques such as Transformer models, GANs, and other deep learning paradigms. Join us at AryaXAI where startup culture meets research innovation, driving speed and reliability in AI technologies.,

Company name: Tier 1 TOP GLOBAL MNC IT company. Location: Bangalore, Pune, ( Hybrid ) As a level in our Consultant/Senior Consultant/Manager youll build and nurture positive working relationships with teams and clients with the intention to exceed client expectations: - We are seeking skilled ML-Ops Manager / Senior Consultants to implement and manage ML-Ops frameworks for various industries. The ideal candidates will have a minimum of 8+ Years (Manager)/4-7 Years of experience (Senior Consultant) in Designing, Implementing, and leading ML-Ops frameworks. In this role, you will be responsible for the deployment, testing, and continuous integration of machine learning models while working with cross-functional teams. Following are the Key requirements: Collaborate with data scientists, developers, and infrastructure teams to ensure seamless integration of machine learning models into production environments. Develop and implement ML-Ops best practices to streamline the end-to-end machine learning lifecycle, from model training and testing to deployment and monitoring. Design, build, and maintain scalable and reliable machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, and deployment. Implement and manage continuous integration and continuous deployment (CI/CD) processes for machine learning models. Monitor the health and performance of deployed models, identifying and addressing issues related to data drift, model degradation, and performance bottlenecks. Automate model retraining and deployment processes to ensure models remain up to date with changing data and requirements. Collaborate with cross-functional teams to ensure data security, compliance, and privacy standards are met throughout the ML-Ops lifecycle. Drive improvements in the machine learning infrastructure by evaluating new tools, technologies, and processes. Participate in on-call rotations to address critical system issues as they arise. Additional Preferred Skills: Consulting/Strategy, Analytics and M&A(SAMA)/AI&Data |Consultant/Senior Consultant/Manager Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) for tracking model performance and system health. Knowledge of machine learning model monitoring and management platforms. Background in distributed computing and parallel processing. Domain expertise in Telecom, Retail / BFSI, Marketing, Life science, Manufacturing etc. Desired qualifications Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field. Proven experience in deploying and managing machine learning models in production environments. Strong programming skills in languages such as Python, and experience with relevant libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Hands-on experience with containerization technologies such as Docker and orchestration platforms like Kubernetes. Proficiency in designing and optimizing data pipelines for large-scale data processing and machine learning. Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and services for ML model deployment and management. Experience with version control systems (e.g., Git) and CI/CD tools (e.g., Jenkins, GitLab CI/CD). Strong understanding of DevOps principles and practices, and the ability to collaborate effectively with cross-functional teams. Excellent problem-solving skills and the ability to diagnose and resolve complex technical issues. Strong communication skills, both written and verbal, to effectively convey technical information to non-technical stakeholders.

As a Machine Learning Engineer at our company based in Coimbatore, Tamil Nadu, India, your primary responsibility will be to design, implement, and deploy machine learning models. You will work closely with data scientists to transition prototypes into production-ready systems. Managing and automating the end-to-end machine learning lifecycle will be a key part of your role. Your duties will include implementing continuous integration and continuous deployment (CI/CD) pipelines customized for machine learning workflows. Monitoring the real-time performance of deployed models, managing model drift, and executing retraining strategies will also be crucial aspects of your responsibilities. Ensuring reproducibility, traceability, and versioning for machine learning models and datasets will be essential. Additionally, you will be tasked with optimizing the machine learning infrastructure for enhanced performance, scalability, and cost-efficiency. It will be important to stay abreast of the latest trends and tools in the field of machine learning and MLOps to continuously improve our practices. To excel in this role, you will need strong programming skills, particularly in Python, along with familiarity with ML frameworks such as TensorFlow and PyTorch. Experience with data processing tools like Pandas and Scikit-learn, as well as CI/CD tools like Jenkins or GitLab CI, will be beneficial. Proficiency in containerization technologies like Docker, orchestration tools like Kubernetes, and ML model versioning tools like MLflow or DVC is also desired. Knowledge of cloud platforms such as AWS, Google Cloud, or Azure and their ML deployment services will be advantageous. The ideal candidate will have previous experience in a machine learning or data science role, along with expertise in advanced monitoring and logging tools tailored for ML models. Possessing certification or training in MLOps, machine learning, or related fields, as well as a background in software development or software engineering, will be beneficial. Familiarity with CRM & ERP systems and their data structures, along with a strong understanding of the latest machine learning trends and techniques, is highly valued. In this role, you will have the opportunity to work with a team of smart individuals in a friendly and open culture. There are no cumbersome managers or unnecessary tools to deal with, and rigid working hours are not a part of our work environment. You will have real responsibilities and the chance to expand your knowledge across various business sectors. By creating content that benefits our users on a daily basis, you will face real challenges in a rapidly evolving company environment.,

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.,

As an AI/ML Engineer at Neutrinos, you will play a crucial role in designing, developing, and deploying machine learning models to drive business outcomes. With 5 to 8 years of hands-on experience, you will utilize your expertise in Python, AutoML frameworks, image processing, and cloud-based AI services to create innovative solutions. Collaborating with cross-functional teams, you will focus on model development, integration, and optimization to deliver high-quality results aligned with business objectives. Your responsibilities will include designing, training, and deploying complex machine learning models for predictive analytics, object detection, and classification. Leveraging AI frameworks like Python, TensorFlow, and PyTorch, you will build and optimize machine learning pipelines. You will also work on implementing and customizing AutoML frameworks to automate model building and enhance efficiency. Utilizing cloud-based AI platforms such as Google Vision, Azure Document Vision, and AWS Textract, you will handle tasks like image recognition and document processing. In addition, you will develop and integrate AI models with REST APIs using NodeJS and Python to ensure efficient communication between systems and scalable deployment. Your role will involve implementing advanced object detection algorithms for various image-based applications and continuously optimizing model performance for accuracy, latency, and scalability in production environments. Utilizing Git for version control and CI/CD practices, you will ensure seamless updates and rollouts of AI solutions. Preferred skills for this role include familiarity with cloud infrastructure such as AWS, Azure, or Google Cloud, experience with Docker and Kubernetes for containerized AI solutions, and knowledge of Machine Learning Operations (MLOps) for automating ML lifecycle processes. A Bachelors or Masters degree in Computer Science, Machine Learning, AI, or a related field, along with 5 to 8 years of experience in AI/ML engineering, will be essential qualifications for this position. Join Neutrinos to be part of a dynamic team at the forefront of AI and machine learning innovation. You will have the opportunity to work on challenging projects with real-world impact, competitive salary and benefits, and experience a collaborative and growth-focused environment in Bangalore.,

About us Bain & Company is a global management consulting firm that assists the world's most ambitious change makers in defining the future. With offices in 65 locations across 40 countries, we collaborate with our clients as a unified team, striving to achieve extraordinary results, surpass the competition, and reshape industries. Since our establishment in 1973, we have gauged our prosperity by the achievements of our clients and uphold the highest level of client advocacy in the industry. In 2004, we inaugurated our presence in the Indian market by launching the Bain Capability Center (BCC) in New Delhi, which is now recognized as BCN (Bain Capability Network) with nodes spread across various geographies. BCN serves as a core and largest unit of Expert Client Delivery (ECD), playing a vital role in enhancing Bain's case teams globally by providing analytics and research solutions in all industries, specific domains for corporate cases, client development, private equity diligence, or Bain intellectual property. The BCN encompasses Consulting Services, Knowledge Services, and Shared Services. Who you will work with Bain & Company stands as the foremost consulting partner to the private equity industry and its stakeholders. The Private Equity Group (PEG) at Bain offers comprehensive advisory services to investors throughout the entire investment lifecycle, including pre-investment strategy, due diligence, post-acquisition value creation, and portfolio management. The PEG Innovation Team (PIT) is a specialized division devoted to supporting the private equity sector and its stakeholders. An essential focus area involves harnessing Generative AI to develop innovative solutions that streamline due diligence, automate processes, and enable data-driven decision-making. By integrating expertise in due diligence, operational enhancement, and cutting-edge technologies like Generative AI, PIT empowers private equity clients to achieve superior returns and maintain a competitive edge in the market. What you'll do This role presents an opportunity to be part of the BCN Data business expanding science capability area within the PIT (PEG Innovation Team). Product Support: The primary responsibility of the team will be to support the Beta production of AI applications for PEG (Private Equity Group) as part of Bain's DD2030 (Due Diligence 2030) initiative. Generative AI Focus: The role involves a significant focus on Generative AI applications, aiming to push the boundaries of innovation in the private equity domain. Broader Automation: Additionally, the role may entail contributing to broader PIT automation initiatives aimed at streamlining processes and enhancing efficiency across various stages of the investment lifecycle. The person in this role will need to: - Translate business objectives into data and analytics solutions, and translate results into business insights using appropriate data engineering, analytics, visualization & Gen AI applications. - Utilize Gen AI skills to design and create repeatable analytical solutions to enhance data quality. - Build and deploy machine learning models using Scikit-Learn for various predictive analytics tasks. - Implement and fine-tune NLP models with Hugging Face to tackle complex language processing challenges. - Collaborate with engineering team members on design requirements to transform PoC methods into repeatable data pipelines; collaborate with the Practice team to develop scalable products. - Aid in creating and documenting standard operating procedures for recurring data processes, and establish a knowledge base of data methods. - Stay updated on advancements in AI/ML technologies and best practices in data science, particularly in LLMs and generative AI. About you A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Applied Mathematics, Econometrics, Statistics, Physics, Market Research, or related field is preferred. 3-5 years of experience in data science & data engineering with hands-on experience in AI/GenAI tools such as Scikit-Learn, LangChain, and Hugging Face. Experience in designing and developing RESTful and GraphQL APIs to facilitate data access and integration. Proficiency in data wrangling in either R or Python is mandatory. Proficiency in SQL is essential, and familiarity with NoSQL data stores is a plus. Familiarity with MLOps practices for model lifecycle management. Experience with Git and modern software development workflow is advantageous. Experience with containerization such as Docker/Kubernetes is beneficial. Proficiency with Agile way of working and tools (Jira, Confluence, Miro). Strong interpersonal and communication skills are imperative. Experience with Microsoft Office suite (Word, Excel, PowerPoint, Teams) is preferred. Ability to explain and discuss Gen AI and Data Engineering technicalities to a business audience. What makes us a great place to work We take pride in consistently being acknowledged as one of the world's best places to work, a proponent of diversity, and a model of social responsibility. Currently ranked as the #1 consulting firm on Glassdoor's Best Places to Work list, we have maintained a position in the top four for the past 12 years. We believe that diversity, inclusion, and collaboration are pivotal in constructing exceptional teams. We recruit individuals with exceptional talents, abilities, and potential, fostering an environment where you can realize your full potential and thrive both professionally and personally. Our commitment to diversity and inclusion has been externally recognized by Fortune, Vault, Mogul, Working Mother, Glassdoor, and the Human Rights Campaign.,

Role & rJob Title: Senior Machine Learning Engineer Anomaly Detection & Time Series Forecasting Location: Bangalore Department: Engineering – AI/ML Job Overview: Resolve Tech Solutions is seeking an experienced Senior Machine Learning Engineer to drive the development of AI-driven anomaly detection, time series forecasting, and predictive analytics models for our next-generation observability platform. This role will focus on designing, building, and deploying ML models that provide real-time insights, predictive alerts, and intelligent recommendations powered by LLMs. As a key individual contributor, you will work on solving complex challenges in large-scale cloud environments while collaborating with cross-functional teams. Key Responsibilities: Develop Advanced ML Models: Design and implement machine learning models for anomaly detection, time series forecasting, and predictive analytics, ensuring high accuracy and scalability. Anomaly Detection & Root Cause Analysis: Build robust models to detect abnormal patterns in metric data, leveraging statistical methods, deep learning, and AI-driven techniques. Time Series Forecasting: Implement predictive models to forecast metric trends, proactively identifying threshold breaches and alerting users. LLM-Driven Insights: Utilize Large Language Models (LLMs) to analyze historical incidents, correlate anomalies, and provide recommendations by integrating with ITSM platforms like ServiceNow. Cloud & Big Data Integration: Work with large-scale data pipelines, integrating ML models with cloud platforms such as AWS, Azure, and GCP. Feature Engineering & Data Processing: Design and optimize feature extraction and data preprocessing pipelines for real-time and batch processing. Model Deployment & Optimization: Deploy ML models in production environments using MLOps best practices, ensuring efficiency, scalability, and reliability. Performance Monitoring & Continuous Improvement: Establish key performance metrics, monitor model drift, and implement retraining mechanisms for continuous model improvement. Collaboration & Knowledge Sharing: Work closely with product managers, data engineers, and DevOps teams to align ML solutions with business objectives and platform goals. Requirements: Experience: 5+ years of hands-on experience in machine learning, with a strong focus on anomaly detection, time series forecasting, and deep learning. ML & AI Expertise: Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, and XGBoost. Anomaly Detection: Experience with statistical techniques, autoencoders, GANs, or isolation forests for anomaly detection in time series data. Time Series Forecasting: Strong background in models such as ARIMA, Prophet, LSTMs, or Transformers for predictive analytics. LLMs & NLP: Hands-on experience in fine-tuning and integrating LLMs for intelligent insights and automated issue resolution. Cloud & Data Engineering: Familiarity with cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI) and distributed computing frameworks like Spark. MLOps & Deployment: Experience with CI/CD pipelines, Docker, Kubernetes, and model monitoring in production. Problem-Solving & Analytical Skills: Ability to analyze large datasets, derive insights, and build scalable ML solutions for enterprise applications. Communication & Collaboration: Strong verbal and written communication skills, with the ability to explain ML concepts to non-technical stakeholders. Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field. Preferred Qualifications: Experience with AIOps, observability, or IT operations analytics. Hands-on experience with reinforcement learning or graph neural networks. Familiarity with Apache Kafka, Flink, or other real-time data processing frameworks. Contributions to open-source ML projects or research publications in anomaly detection and time series analysis. Why Join Us? Opportunity to work on cutting-edge AI/ML solutions for enterprise observability. Collaborative and innovative work environment with top AI/ML talent. Competitive salary, benefits, and career growth opportunities. Exposure to large-scale, cloud-native, and AI-driven technologies. If you are passionate about AI-driven anomaly detection, time series forecasting, and leveraging LLMs for real-world enterprise solutions, we'd love to hear from you!esponsibilities Preferred candidate profile

We are seeking a skilled Data Scientist with expertise in AI orchestration and embedded systems to support a sprint-based Agile implementation focused on integrating generative AI capabilities into enterprise platforms such as Slack, Looker, and Confluence. The ideal candidate will have hands-on experience with Gemini and a strong understanding of prompt engineering, vector databases, and orchestration infrastructure. Key Responsibilities : - Develop and deploy Slack-based AI assistants leveraging Gemini models. - Design and implement prompt templates tailored to enterprise data use cases (Looker and Confluence). - Establish and manage an embedding pipeline for Confluence documentation. - Build and maintain orchestration logic for prompt execution and data retrieval. - Set up API authentication and role-based access controls for integrated systems. - Connect and validate vector store operations (e.g., Pinecone, Weaviate, or Snowflake vector extension). - Contribute to documentation, internal walkthroughs, and user acceptance testing planning. - Participate in Agile ceremonies including daily standups and sprint demos. Required Qualifications : - Proven experience with Gemini and large language model deployment in production environments. - Proficiency in Python, orchestration tools, and prompt engineering techniques. - Familiarity with vector database technologies and embedding workflows. - Experience integrating APIs for data platforms such as Looker and Confluence. - Strong understanding of access control frameworks and enterprise-grade authentication. - Demonstrated success in Agile, sprint-based project environments. Preferred Qualifications : - Experience working with Slack app development and deployment. - Background in MLOps, LLMOps, or AI system orchestration at scale. - Excellent communication skills and ability to work in cross-functional teams.

Responsibilities: Design and implement robust MLOps pipelines for model training, evaluation, deployment, and monitoring using industry-standard tools and frameworks Collaborate with data scientists to streamline the model development process and ensure seamless integration with MLOps pipelines. Optimize and scale machine learning infrastructure to support high-performance model training and inference. Contribute to the development of MLOps standards, processes, and documentation within the organization. Mentor and support junior team members in MLOps practices and technologies. Stay up-to-date with the latest trends and best practices in MLOps, and explore opportunities for continuous improvement. Qualifications: Bachelor's or Master's degree in Computer Science, Statistics, or a related field. 5+ years of experience in software engineering, with 2+ years experience in ML Proficient in Python and at least one other programming language (e.g., Java, Go, C++). Extensive experience with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure). Familiarity with machine learning frameworks and MLOps tools Experience with big data technologies Strong understanding of CI/CD principles and practices.

Roles and Responsibilities 1. MLOps Strategy & Implementation Design and implement scalable MLOps pipelines for the end-to-end lifecycle of machine learning models (from data ingestion to model deployment and monitoring). Automate model training, testing, validation, and deployment using CI/CD practices. Collaborate with data scientists to productize ML models. 2. Infrastructure Management Build and maintain cloud-native infrastructure (e.g., AWS/GCP/Azure) for training, deploying, and monitoring ML models. Optimize compute and storage resources for ML workloads. Containerize ML applications using Docker and orchestrate them with Kubernetes. 3. Model Monitoring & Governance Set up monitoring for ML model performance (drift detection, accuracy drop, latency). Ensure compliance with ML governance policies, versioning, and auditing. 4. Collaboration & Communication Work with cross-functional teams (Data Engineering, DevOps, and Product) to ensure smooth ML model deployment and maintenance. Provide mentorship and technical guidance to junior engineers. 5. Automation & Optimization Automate feature extraction, model retraining, and deployment processes. Improve latency, throughput, and efficiency of deployed models in production. Technical Skills / Tech Stack 1. Programming Languages Python (primary for ML/AI and scripting) Bash/Shell Go or Java (optional but valuable for performance-critical components) 2. ML Frameworks & Libraries TensorFlow , PyTorch , Scikit-learn MLflow , Kubeflow , or SageMaker ONNX (for model conversion) 3. Data & Pipeline Tools Apache Airflow , Luigi Kafka , Apache Beam , Spark (for streaming/batch data) Pandas , Dask , NumPy 4. DevOps & MLOps Tools Docker , Kubernetes , Helm Terraform , Pulumi (for infrastructure as code) Jenkins , GitHub Actions , Argo Workflows MLflow , DVC , Tecton , Feast 5. Cloud Platforms AWS : S3, EKS, SageMaker, Lambda, CloudWatch GCP : GKE, AI Platform, BigQuery, Dataflow Azure : Azure ML, AKS, Blob Storage 6. Monitoring & Logging Prometheus , Grafana ELK Stack , Datadog , Cloud-native monitoring tools 7. CI/CD & Versioning Git , GitOps , CI/CD pipelines for model and data versioning Preferred Experience 5+ years in AI/ML engineering roles. Experience building MLOps pipelines in production. Familiarity with regulatory and ethical considerations in ML (e.g., fairness, bias detection, explainability). Strong debugging and performance tuning skills in distributed environments.

Hi All Job Title: MLOPS Engineer Exp: 5+ Years Location: Hyderabad, Trivandrum, Kochi Mandatory Skillset - MLOps, Python, AWS, CI CD pipeline