225 Mlops Jobs

Key Responsibilities Responsible for building and maintaining robust machine learning pipelines ensuring efficient model deployment monitoring and lifecycle management within a cloud-based environment Extensive expertise in MLOps specifically with Google Cloud Platform GCP and Vertex AI and a deep understanding of model performance drift detection and GPU accelerators Build and maintain scalable MLOps pipelines in GCP Vertex AI for endtoend machine learning workflows Manage the full MLOps lifecycle from data preprocessing model training and deployment to model monitoring and drift detection Implement realtime model monitoring and drift detection to ensure optimal model performance over time Optimize model training and inference processes using GPU accelerators and CUDA Collaborate with cross functional teams to automate and streamline machine learning model deployment and monitoring Utilize Python 310 with libraries such as pandas NumPy and TensorFlow to handle data processing and model development Set up infrastructure for continuous training testing and deployment of machine learning models Ensure scalability security and high availability in all machine learning operations by implementing best practices in MLOps Requirements 5 years of experience in MLOps and building ML pipelines 3 years of experience in GCP Vertex AI Deep understanding of the MLOps lifecycle and automation of ML workflows Proficient in Python 310 and related libraries such as pandas NumPy and TensorFlow Strong experience in GPU accelerators and CUDA for model training and optimization Proven experience in model monitoring drift detection and maintaining model accuracy over time Strong problemsolving skills with the ability to work in a fast paced environment Knowledge of data versioning and model version control techniques Familiarity with TensorFlow Extended TFX or other ML workflow orchestration frameworks

What you will do We are seeking a highly skilled Machine Learning Engineer with a strong MLOps background to join our team. You will play a pivotal role in building and scaling our machine learning models from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable ML pipelines. Roles & Responsibilities: Collaborate with data scientists to develop, train, and evaluate machine learning models. Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring. Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment. Implement DevOps/MLOps best practices to automate ML workflows and improve efficiency. Develop and implement monitoring systems to track model performance and identify issues. Conduct A/B testing and experimentation to optimize model performance. Work closely with data scientists, engineers, and product teams to deliver ML solutions. Guide and mentor junior engineers in the team Stay updated with the latest trends and advancements Basic Qualifications: Doctorate degree and 2 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR Masters degree and 8 to 10 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR Bachelors degree and 10 to 14 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR Diploma and 14 to 18 years of years of Computer Science, Statistics, and Data Science, Machine Learning experience Preferred Qualifications: Must-Have Skills: Strong foundation in machine learning algorithms and techniques Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD) Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn) Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills Good-to-Have Skills: Experience with big data technologies (e.g., Spark), and performance tuning in query and data processing Experience with data engineering and pipeline development Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification Knowledge of NLP techniques for text analysis and sentiment analysis Experience in analyzing time-series data for forecasting and trend analysis Familiar with AWS, Azure, or Google Cloud; Familiar with Databricks platform for data analytics and MLOps Professional Certifications Cloud Computing and Databricks certificate preferred Soft Skills: Excellent analytical and fixing skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills.

Role & responsibilities 3+ years of experience in Spark, Databricks, Hadoop, Data and ML Engineering. 3+ Years on experience in designing architectures using AWS cloud services & Databricks. Architecture, design and build Big Data Platform (Data Lake / Data Warehouse / Lake house) using Databricks services and integrating with wider AWS cloud services. Knowledge & experience in infrastructure as code and CI/CD pipeline to build and deploy data platform tech stack and solution. Hands-on spark experience in supporting and developing Data Engineering (ETL/ELT) and Machine learning (ML) solutions using Python, Spark, Scala or R languages. Distributed system fundamentals and optimising Spark distributed computing. Experience in setting up batch and streams data pipeline using Databricks DLT, jobs and streams. Understand the concepts and principles of data modelling, Database, tables and can produce, maintain, and update relevant data models across multiple subject areas. Design, build and test medium to complex or large-scale data pipelines (ETL/ELT) based on feeds from multiple systems using a range of different storage technologies and/or access methods, implement data quality validation and to create repeatable and reusable pipelines Experience in designing metadata repositories, understanding range of metadata tools and technologies to implement metadata repositories and working with metadata. Understand the concepts of build automation, implementing automation pipelines to build, test and deploy changes to higher environments. Define and execute test cases, scripts and understand the role of testing and how it works. Preferred candidate profile Big Data technologies Databricks, Spark, Hadoop, EMR or Hortonworks. Solid hands-on experience in programming languages Python, Spark, SQL, Spark SQL, Spark Streaming, Hive and Presto Experience in different Databricks components and API like notebooks, jobs, DLT, interactive and jobs cluster, SQL warehouse, policies, secrets, dbfs, Hive Metastore, Glue Metastore, Unity Catalog and ML Flow. Knowledge and experience in AWS Lambda, VPC, S3, EC2, API Gateway, IAM users, roles & policies, Cognito, Application Load Balancer, Glue, Redshift, Spectrum, Athena and Kinesis. Experience in using source control tools like git, bit bucket or AWS code commit and automation tools like Jenkins, AWS Code build and Code deploy. Hands-on experience in terraform and Databricks API to automate infrastructure stack. Experience in implementing CI/CD pipeline and ML Ops pipeline using Git, Git actions or Jenkins. Experience in delivering project artifacts like design documents, test cases, traceability matrix and low-level design documents. Build references architectures, how-tos, and demo applications for customers. Ready to complete certifications

Description: Strong experience in ETL development, data modeling, and managing data in large-scale environments. - Proficient in AWS services including SageMaker, S3, Glue, Lambda, and CloudFormation/Terraform. - Hands-on expertise with MLOps best practices, including model versioning, monitoring, and CI/CD for ML pipelines. - Proficiency in Python and SQL; experience with Java is a plus for streaming jobs. - Deep understanding of cloud infrastructure automation using Terraform or similar IaC tools. - Excellent problem-solving skills with the ability to troubleshoot data processing and deployment issues. - Experience in fast-paced, agile development environments with frequent delivery cycles. - Strong communication and collaboration skills to work effectively across cross-functional team Role & responsibilities Preferred candidate profile

Greetings from Sight Spectrum Technologies!!! We would like to ensure that you are interested in this position. Company: Sight Spectrum Technologies( https://sightspectrum.com/ ) Experience : 5+Years Location: Chennai, Bangalore, hyderabad, Coimbatore Description: Strong experience in ETL development, data modeling, and managing data in large-scale environments. - Proficient in AWS services including SageMaker, S3, Glue, Lambda, and CloudFormation/Terraform. - Hands-on expertise with MLOps best practices, including model versioning, monitoring, and CI/CD for ML pipelines. - Proficiency in Python and SQL; experience with Java is a plus for streaming jobs. - Deep understanding of cloud infrastructure automation using Terraform or similar IaC tools. - Excellent problem-solving skills with the ability to troubleshoot data processing and deployment issues. - Experience in fast-paced, agile development environments with frequent delivery cycles.- Strong communication and collaboration skills to work effectively across cross-functional teams Please fill the below details for reference. Total Experience: Relevant Experience: Current CTC: Expected CTC: Notice Period (LWD): Current Location: Preferred Location: Payroll Company: Reason for change: Client Company: Offer Details: PF(Yes/No): UAN No: Linkdln Id: If interested kindly share your resume to roopavahini@sightspectrum.in

We are looking for a highly motivated Senior DevOps/Mlops engineer specializing in AWS and Kubernetes to join our team Required Candidate profile Exp. in AI OPERATIONS, MLops & Python (MUST). Exp. with deploying secure infrastructure & services in one or more cloud environments such as AWS (must), Azure or GCP Exp. in Kubernetes

Looking for a skilled Senior Data Science Engineer with 6-12 years of experience to lead the development of advanced computer vision models and systems. The ideal candidate will have hands-on experience with state-of-the-art architectures and a deep understanding of the complete ML lifecycle. This position is based in Bengaluru. Roles and Responsibility Lead the development and implementation of computer vision models for tasks such as object detection, tracking, image retrieval, and scene understanding. Design and execute end-to-end pipelines for data preparation, model training, evaluation, and deployment. Perform fine-tuning and transfer learning on large-scale vision-language models to meet application-specific needs. Optimize deep learning models for edge inference (NVIDIA Jetson, TensorRT, OpenVINO) and real-time performance. Develop scalable and maintainable ML pipelines using tools such as MLflow, DVC, and Kubeflow. Automate experimentation and deployment processes using CI/CD workflows. Collaborate cross-functionally with MLOps, backend, and product teams to align technical efforts with business needs. Monitor, debug, and enhance model performance in production environments. Stay up-to-date with the latest trends in CV/AI research and rapidly prototype new ideas for real-world use. Job Requirements 6-7+ years of hands-on experience in data science and machine learning, with at least 4 years focused on computer vision. Strong experience with deep learning frameworks: PyTorch (preferred), TensorFlow, Hugging Face Transformers. In-depth understanding and practical experience with Class-incremental learning and lifelong learning systems. Proficient in Python, including data processing libraries like NumPy, Pandas, and OpenCV. Strong command of version control and reproducibility tools (e.g., MLflow, DVC, Weights & Biases). Experience with training and optimizing models for GPU inference and edge deployment (Jetson, Coral, etc.). Familiarity with ONNX, TensorRT, and model quantization/conversion techniques. Demonstrated ability to analyze and work with large-scale visual datasets in real-time or near-real-time systems. Experience working in fast-paced startup environments with ownership of production AI systems. Exposure to cloud platforms such as AWS (SageMaker, Lambda), GCP, or Azure for ML workflows. Experience with video analytics, real-time inference, and event-based vision systems. Familiarity with monitoring tools for ML systems (e.g., Prometheus, Grafana, Sentry). Prior work in domains such as retail analytics, healthcare, or surveillance/IoT-based CV applications. Contributions to open-source computer vision libraries or publications in top AI/ML conferences (e.g., CVPR, NeurIPS, ICCV). Comfortable mentoring junior engineers and collaborating with cross-functional stakeholders.

We are looking for a skilled AI Engineer with 3 to 8 years of experience in software engineering or machine learning to design, implement, and productionize LLM-powered agents that solve real-world enterprise problems. This position is based in Kolkata. Roles and Responsibility Architect and build multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, Google ADK, Palantir Foundry, or custom orchestration layers. Fine-tune and prompt-engineer LLMs (OpenAI, Anthropic, open-source) for retrieval-augmented generation (RAG), reasoning, and tool use. Integrate agents with enterprise data sources (APIs, SQL/NoSQL DBs, vector stores like Pinecone, Elasticsearch) and downstream applications (Snowflake, ServiceNow, custom APIs). Own the MLOps lifecycle: containerize (Docker), automate CI/CD, monitor drift & hallucinations, set up guardrails, observability, and rollback strategies. Collaborate cross-functionally with product, UX, and customer teams to translate requirements into robust agent capabilities and user-facing features. Benchmark and iterate on latency, cost, and accuracy; design experiments, run A/B tests, and present findings to stakeholders. Job Requirements Strong Python skills (async I/O, typing, testing) plus familiarity with TypeScript/Node or Go is a bonus. Hands-on experience with at least one LLM/agent framework and platform (LangChain, LangGraph, Google ADK, LlamaIndex, Emma, etc.). Solid grasp of vector databases (Pinecone, Weaviate, FAISS) and embedding models. Experience building and securing REST/GraphQL APIs and microservices. Cloud skills on AWS, Azure, or GCP (serverless, IAM, networking, cost optimization). Proficient with Git, Docker, CI/CD (GitHub Actions, GitLab CI, or similar).

Role - Machine Learning Engineer Required Skills & Experience 5+ years of hands-on experience in building, training, and deploying machine learning models in a professional, production-oriented setting. Demonstrable experience with database creation and advanced querying (e.g., SQL, NoSQL), with a strong understanding of data warehousing concepts. Proven expertise in data blending, transformation, and feature engineering , adept at integrating and harmonizing both structured (e.g., relational databases, CSVs) and unstructured (e.g., text, logs, images) data. Strong practical experience with cloud platforms for machine learning development and deployment; significant experience with Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, Dataflow) is highly desirable. Proficiency in programming languages commonly used in data science (e.g., Python is preferred, R). Solid understanding of various machine learning algorithms (e.g., regression, classification, clustering, dimensionality reduction) and experience with advanced techniques like Deep Learning, Natural Language Processing (NLP), or Computer Vision . Experience with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch ). Familiarity with MLOps tools and practices , including model versioning, monitoring, A/B testing, and continuous integration/continuous deployment (CI/CD) pipelines. Experience with containerization technologies like Docker and orchestration tools like Kubernetes for deploying ML models as REST APIs. Proficiency with version control systems (e.g., Git, GitHub/GitLab) for collaborative development. Interested candidates share cv to dikshith.nalapatla@motivitylabs.com

Job Description: We are seeking a talented and experienced Data Scientist to join our dynamic team. The ideal candidate will have a strong background in data analysis, machine learning, statistical modeling, and artificial intelligence. Experience with Natural Language Processing (NLP) is desirable. Experience delivering products that incorporate AI/ML, familiarity with Cloud Services such as AWS highly desirable. Required Skills/Qualifications : - 3-12 years of experience in AI/ML related work - Extensive experience in Python - Familiarity with Statistical models such as Linear/Logistic regression, Bayesian Models, Classification/Clustering models, Time Series analysis - Experience with deep learning models such as CNNs, RNNs, LSTM, Transformers - Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras - Experience with GenAI, LLMs, RAG architecture would be a plus - Familiarity with cloud services such as AWS, Azure - Familiarity with version control systems (e.g., Git), JIRA, Confluence - Familiarity with MLOPs concepts, AI/ML pipeline tooling such as Kedro - Knowledge of CI/CD pipelines and DevOps practices - Experience delivering customer facing AI Solutions delivered as SaaS would be a plus - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - Strong problem-solving skills and attention to detail - Excellent verbal and written communication and teamwork skills

Hi, Hope you are doing great!!! Rockline Tech Champ Ind Private Limited is a leading IT consulting and technology solutions provider for global mid-to-large enterprises. The company offers a dynamic, innovation-driven environment and is looking for experienced professionals who thrive on challenges and aim to make a real impact in a fast-paced, quality-focused team. Req: AI OPS Engineer Location: Pune and Bangalore - Hybrid at office Experience: 5yrs -8yrs Skill Set: CI/CD pipelines orchestration by Airflow, Azure Devops "Designing and implementing cloud solutions, build MLOps on cloud (AWS, Azure) , Data Science , Python, Bash, Pytorch, TensorFlow, KubeFlow, MLFlow, Docker, Kubernetes, Openshift, Git Hub Mode: Contract to Hire - (Initially 6-8 months Rockline Tech Champ Ind Private Limited payroll after that you will be Perm with client) ****************** Need resource who can join in Immediately/ 15 days****************** ****************** PF Cut mandatory 3600rs****************** Kindly share below details. 1) Pan card No: 2) DOB: 3) First name: 4) Last name: 5) Contact number: 6) Emergency contact number(relationship): 7) Current CTC: 8) Expected CTC: 9) Notice Period: 10) Current location: 11) Preferred location: 12) Total /Rel experience(in Years): 13) Relevant Experience (in years): 14) Can Join (Client looking who can join Immediate/ 15 Days): 15) Pls mention reason for interested Contract to Hire role: 16) Current Company (If any payroll: Pls mention): 17) PF cut mandatory: Pls share UAN number

Responsibilities Technical knowledge- has expertise in cloud technologies, specifically MS Azure, and services with hands on coding to Expertise in Object Oriented Python Programming with 6 -8 years experience. DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum Hands-On MS Azure Cloud knowledge Understand and take requirements on Operationalization of ML Models from Data Scientist Help team with ML Pipelines from creation to execution List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup Assist team to coding standards (flake8 etc) Guide team to debug on issues with pipeline failures Engage with Business / Stakeholders with status update on progress of development and issue fix Automation, Technology and Process Improvement for the deployed projects Setup Standards related to Coding, Pipelines and Documentation Adhere to KPI / SLA for Pipeline Run, Execution Research on new topics, services and enhancements in Cloud TechnologiesDomain / Technical / Tools Knowledge: Object oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation Experience in Test Driven Development and experience in using Pytest frameworks, git version control, Rest APIs Python programming with OOPs concept, SQL, XML, YAML, Bash, JSON, Pydantic models, Class based frameworks, Dependency injections FastAPI, Flask, Streamlit, Python, Azure API management, API Gateways, Traffic Manager, Load Balancers Nginx, Uvicorn, Gunicorn, Azure ML best practices in environment management, run time configurations (Azure ML & Databricks clusters), alerts. Experience designing and implementing ML Systems & pipelines, MLOps practices and tools such a MLFlow, Kubernetes, etc. Exposure to event driven orchestration, Online Model deployment Contribute towards establishing best practices in MLOps Systems development Proficiency with data analysis tools (e.g., SQL, R & Python) High level understanding of database concepts/reporting & Data Science concepts Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team Experience in managing client relationship and developing business cases for opportunities Azure AZ-900 Certification with Azure Architect Technical and Professional Requirements: Primary skills:Technology->OpenSystem->Python - OpenSystemResponsible for successful delivery of MLOps solutions and services in client consulting environments; Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client.Assist clients with operationalization metrics to track performance of ML ModelsAgile trained to manage team effort and track through JIRAHigh Impact Communication- Assesses the target audience need, prepares and practices a logical flow, answers audience questions appropriately and sticks to timeline. Preferred Skills: Technology->OpenSystem->Python - OpenSystem->Python Additional Responsibilities: Good knowledge on software configuration management systems Strong business acumen, strategy and cross-industry thought leadership Awareness of latest technologies and Industry trendsEducation and Experience: Overall, 6 to 8 years of experience in Data driven software engineering with 3-5 years of experience designing, building and deploying enterprise AI or ML applications with at least 2 years of experience implementing full lifecycle ML automation using MLOps(scalable development to deployment of complex data science workflows) Bachelors or Masters degree in Computer Science Engineering or equivalent Domain experience in Retail, CPG and Logistics etc. Azure Certified DP100, AZ/AI900 Logical thinking and problem solving skills along with an ability to collaborate Two or three industry domain knowledge Understanding of the financial processes for various types of projects and the various pricing models available Client Interfacing skills Knowledge of SDLC and agile methodologies Project and Team management Educational Requirements MCA,MSc,Bachelor of Engineering,BBA,BCom Service Line Data & Analytics Unit * Location of posting is subject to business requirements

Responsibilities Responsibilities:- Assess the complete ML development process.- Data Engineering- Evaluate the maturity level of ML systems. - Software Development- Provide recommendations for improvement - DevOps- Implement and maintain CI/CD pipelines for ML models. - Monitor and manage model performance in production. Technical and Professional Requirements: - 3+ years in MLOps or related roles - Data Science- Hands-on experience with ML model deployment and monitoring - System Administration- Proven track record of implementing CI/CD pipelines - Automation Tools- Experience with version control systems (Git). Preferred Skills: Technology->Machine Learning->MLOps Educational Requirements Bachelor of Engineering Service Line Quality * Location of posting is subject to business requirements

Roles and Responsibilities Design and develop data science solutions using Python. Experience in machine learning and deep learning frameworks (PyTorch, TensorFlow) Develop machine learning models using TensorFlow or PyTorch and deploy them on Kubernetes, MLflow, and KServe clusters. Familiarity with machine learning deployment and MLOps tools Experience working with model repositories and fine-tuning frameworks like Hugging Face. Experience with frameworks like LangGraph/LangChain. 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.

"We're Hiring For Generative Ai Engineer Role at Bangalore Location" Position: Generative Ai Engineer Experience: 7+ Years Location: Bangalore Responsibilities Develop and deploy scalable big data and AI solutions using Databricks and Azure. Implement RAG pipelines and integrate GenAI APIs for document and image retrieval. Perform finetuning of LLM models and manage prompt engineering workflows. Ensure the end-to-end solution is optimized for performance and scalability. Collaborate with software and ML teams for integration of AI capabilities. Deploy GenAI projects in production in Azure and Databricks. (Must) Required Qualifications Bachelors or Masters degree in Computer Science or related field. 7+ years of experience in big data and AI development. Strong experience with Databricks, Pyspark, and Python. Experience in deploying GenAI projects in production. Preferred Qualifications Familiarity with RAG architecture and document-based retrieval systems. Experience with Azure OpenAI, LangChain, Pinecone or similar tools. Experience with deploying LLM, VLM Models in Cloud Experience with MLOps tools such as MLFlow or similar tools. Technologies Used Python, Pyspark, Databricks, Azure ML, LangChain, OpenAI API, Pinecone, MLFlow More information: +91 73597 10155 | rushit@tekpillar.com

Role & responsibilities : Job Title : AI Engineer (AI-Powered Agents, Knowledge Graphs, & MLOps) Location: Hyderabad Job Type : Full-time Hands-on Gen AI development in GCP and Azure stack Job Summary : We seek an AI Engineer with deep expertise in building AI-powered agents, designing and implementing knowledge graphs, and optimizing business processes through AI-driven solutions. The role also requires hands-on experience in AI Operations (AI Ops), including continuous integration/deployment (CI/CD), model monitoring, and retraining. The ideal candidate will have experience working with open-source or commercial large language models (LLMs) and be proficient in using platforms like Azure Machine Learning Studio or Google Vertex AI to scale AI solutions effectively. Key Responsibilities : AI Agent Development : Design, build, and deploy AI-powered agents for applications such as virtual assistants, customer service bots, and task automation systems using LLMs and other AI models. Knowledge Graph Implementation : Develop and implement knowledge graphs for enterprise data integration, enhancing the retrieval, structuring, and management of large datasets to support decision-making. AI-Driven Process Optimization : Collaborate with business units to optimize workflows using AI-driven solutions, automating decision-making processes and improving operational efficiency. AI Ops (MLOps) : Implement robust AI/ML pipelines that follow CI/CD best practices to ensure continuous integration and deployment of AI models across different environments. Model Monitoring and Maintenance : Establish processes for real-time model monitoring, including tracking performance, drift detection, and accuracy of models in production environments. Model Retraining and Optimization : Develop automated or semi-automated pipelines for model retraining based on changes in data patterns or model performance. Implement processes to ensure continuous improvement and accuracy of AI solutions. Cloud and ML Platforms : Utilize platforms such as Azure Machine Learning Studio, Google Vertex AI, and open-source frameworks for end-to-end model development, deployment, and monitoring. Collaboration : Work closely with data scientists, software engineers, and business stakeholders to deploy scalable AI solutions that deliver business impact. MLOps Tools : Leverage MLOps tools for version control, model deployment, monitoring, and automated retraining processes to ensure operational stability and scalability of AI systems. Performance Optimization : Continuously optimize models for scalability and performance, identifying bottlenecks and improving efficiencies. Qualifications : Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3+ years of experience as an AI Engineer, focusing on AI-powered agent development, knowledge graphs, AI-driven process optimization, and MLOps practices. Proficiency in working with large language models (LLMs) such as GPT-3/4, GPT-J, BLOOM, or similar, including both open-source and commercial variants. Experience with knowledge graph technologies, including ontology design and graph databases (e.g., Neo4j, AWS Neptune). AI Ops/MLOps Expertise : Hands-on experience with AI/ML CI/CD pipelines, automated model deployment, and continuous model monitoring in production environments. Familiarity with tools and frameworks for model lifecycle management, such as MLflow, Kubeflow, or similar. Strong skills in Python, Java, or similar languages, and proficiency in building, deploying, and monitoring AI models. Solid experience in natural language processing (NLP) techniques, including building conversational AI, entity recognition, and text generation models. Model Monitoring & Retraining : Expertise in setting up automated pipelines for model retraining, monitoring for drift, and ensuring the continuous performance of deployed models. Experience in using cloud platforms like Azure Machine Learning Studio, Google Vertex AI, or similar cloud-based AI/ML tools. Preferred Skills : Experience with building or integrating conversational AI agents using platforms like Microsoft Bot Framework, Rasa, or Dialogflow. Familiarity with AI-driven business process automation and RPA integration using AI/ML models. Knowledge of advanced AI-driven process optimization tools and techniques, including AI orchestration for enterprise workflows. Experience with containerization technologies (e.g., Docker, Kubernetes) to support scalable AI/ML model deployment. Certification in Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or relevant MLOps-related certifications is a plus. Preferred candidate profile Perks and benefits

Senior Data Scientist Location: Onsite Bangalore Experience: 8+ years Role Overview We are seeking a Senior Data Scientist with a strong foundation in machine learning, deep learning, and statistical modeling, with the ability to translate complex operational problems into scalable AI/ML solutions. In addition to core data science responsibilities, the role involves building production-ready backends in Python and contributing to end-to-end model lifecycle management. Exposure to computer vision is a plus, especially for industrial use cases like identification, intrusion detection, and anomaly detection. Key Responsibilities Develop, validate, and deploy machine learning and deep learning models for forecasting, classification, anomaly detection, and operational optimization Build backend APIs using Python (FastAPI, Flask) to serve ML/DL models in production environments Apply advanced computer vision models (e.g., YOLO, Faster R-CNN) to object detection, intrusion detection, and visual monitoring tasks Translate business problems into analytical frameworks and data science solutions Work with data engineering and DevOps teams to operationalize and monitor models at scale Collaborate with product, domain experts, and engineering teams to iterate on solution design Contribute to technical documentation, model explainability, and reproducibility practices Required Skills Strong proficiency in Python for data science and backend development Experience with ML/DL libraries such as scikit-learn, TensorFlow, or PyTorch Solid knowledge of time-series modeling, forecasting techniques, and anomaly detection Experience building and deploying APIs for model serving (FastAPI, Flask) Familiarity with real-time data pipelines using Kafka, Spark, or similar tools Strong understanding of model validation, feature engineering, and performance tuning Ability to work with SQL and NoSQL databases, and large-scale datasets Good communication skills and stakeholder engagement experience Good to Have Experience with ML model deployment tools (MLflow, Docker, Airflow) Understanding of MLOps and continuous model delivery practices Background in aviation, logistics, manufacturing, or other industrial domains Familiarity with edge deployment and optimization of vision models Qualifications Masters or PhD in Data Science, Computer Science, Applied Mathematics, or related field 7+ years of experience in machine learning and data science, including end-to-end deployment of models in production

Mandatory skill : MLOps with Azure Databricks / Devops A person who has exposure to Models/MLOps eco-system having exposure to Model Life Cycle Management, with primary responsibility being ability to engage with stakeholders around requirements elaboration, having them broken into stories for the pods by engaging with architects/leads for designs, participation in UAT and creation of user scenario testings, creation of product documentation describing features, capabilities etc. we basically not looking for a Project Manager who will track things.

4+yrs Data science,ML frameworks,MLOps, Python, Data engineering,Cloud platforms,Edge computing,Data manipulation libraries,Model development,Object,Experiment, design,Ab testing. Reach me at mailcv108@gmail.com or WhatsApp me at +91 9611702105

Roles and Responsibilities Design, develop, and deploy advanced AI models with a focus on generative AI, including transformer architectures (e.g., GPT, BERT, T5) and other deep learning models used for text, image, or multimodal generation. Work with extensive and complex datasets, performing tasks such as cleaning, preprocessing, and transforming data to meet quality and relevance standards for generative model training. Collaborate with cross-functional teams (e.g., product, engineering, data science) to identify project objectives and create solutions using generative AI tailored to business needs. Implement, fine-tune, and scale generative AI models in production environments, ensuring robust model performance and efficient resource utilization. Develop pipelines and frameworks for efficient data ingestion, model training, evaluation, and deployment, including A/B testing and monitoring of generative models in production. Stay informed about the latest advancements in generative AI research, techniques, and tools, applying new findings to improve model performance, usability, and scalability. Document and communicate technical specifications, algorithms, and project outcomes to technical and non-technical stakeholders, with an emphasis on explainability and responsible AI practices. Qualifications Required Educational Background : Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or a related field. Relevant Ph.D. or research experience in generative AI is a plus. Experience : 2 - 11 Years of experience in machine learning, with 2+ years in designing and implementing generative AI models or working specifically with transformer-based models. Skills and Experience Required Generative AI : Transformer Models, GANs, VAEs, Text Generation, Image Generation Machine Learning : Algorithms, Deep Learning, Neural Networks Programming : Python, SQL; familiarity with libraries such as Hugging Face Transformers, PyTorch, TensorFlow MLOps : Docker, Kubernetes, MLflow, Cloud Platforms (AWS, GCP, Azure) ? Data Engineering : Data Preprocessing, Feature Engineering, Data Cleaning Why you'll love working with us: BRING YOUR PASSION AND FUN . Corporate culture woven from highly diverse perspectives and insights. BALANCE WORK AND PERSONAL TIME LIKE A BOSS . Resources and flexibility to more easily integrate your work and your life. BECOME A CERTIFIED SMARTY PANTS . Ongoing training and development opportunities for even the most insatiable learner. START-UP SPIRIT (Good ten plus years, yet we maintain it) FLEXIBLE WORKING HOURS

Overview The AI Solution Architect will be responsible for designing and guiding the technical architecture for the rapid delivery of Machine Learning (ML) and Large Language Model (LLM) powered applications. This role requires a strong understanding of end-to-end system design, integration principles, and the ability to translate business needs into clear and scalable technical blueprints. The architect will ensure seamless integration of various components, optimize for scalability, and effectively communicate architectural designs and trade-offs to both technical and non-technical stakeholders. Key Responsibilities: Design and document end-to-end technical architectures for rapid prototyping and Proof-of-Concepts (POCs) of ML and LLM powered applications. Translate complex business requirements into actionable and well-defined technical designs, considering factors such as performance, scalability, security, and maintainability. Architect the integration of various systems, including APIs (REST, gRPC, etc.), databases (SQL, NoSQL), and front-end applications, within tight project timelines and constraints. Define data pipelines and data storage solutions optimized for ML/LLM workloads. Select appropriate technologies, frameworks, and tools for building and deploying AI solutions. Ensure the scalability and reliability of the designed architectures. Collaborate closely with development teams to provide architectural guidance and support throughout the development lifecycle. Communicate architectural designs, trade-offs, and technical complexities clearly and concisely to both technical and non-technical stakeholders, including product managers, business analysts, and executives. Identify and mitigate potential technical risks and challenges. Stay up-to-date with the latest advancements in AI/ML/LLM technologies and architectural patterns. Contribute to the development of architectural best practices and standards within the organization. Requirements Skills & Experience: Proven experience in designing end-to-end technical architectures for rapid prototyping or POCs, with a strong emphasis on ML/LLM components. Demonstrated ability to break down complex business requirements into clear, actionable technical designs and specifications. Hands-on experience integrating multiple systems, including APIs (REST, gRPC, etc.), databases (SQL, NoSQL), and front-end applications, under tight timelines and constraints. Excellent communication and presentation skills, with the ability to effectively articulate complex technical concepts and architectural trade-offs to both technical and non-technical team members. Strong understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and their AI/ML services. Experience with containerization technologies (e.g., Docker, Kubernetes). Familiarity with data engineering principles and tools. Understanding of software development methodologies (e.g., Agile, Scrum). Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field. Preferred Qualifications: Experience with specific ML/LLM frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers). Experience with MLOps practices and tools for deploying and managing ML models. Knowledge of security best practices for AI applications. Experience in a fast-paced startup or innovation-driven environment.

Role & responsibilities We are looking for MLOps engineer position for MNC company permanent position for Hyderabad location. Preferred candidate profile Primary skill : AWS MLOPS Secondary Skill : Python -Automating AI/ML model deployment and Setting up monitoring for the ML pipeline -Integrate the existing, new codebase to the customer pipelines - Automating CI/CD pipelines to account for data, code, and model changes - Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge - Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, FastAPI - Proficiency in tools such as Docker and Kubernetes - Familiarity with experiment tracking frameworks such as MLflow - Setting up and automating data pipelines using tools such as Airflow, Kafka amd Rabbitmq - Providing best practices and executing POC for automated and efficient model operations at scale. - Good to have hands-on experience using large foundation models (e.g. LLMs) and associated tool chains (e.g. langchain) and APIs to build applications, tools and workflows for production. - Ability to talk to customer architect team and drive and own the deployment cycle and co-ordinate with AIML developers to deploy the solution

We are seeking a skilled and experienced Machine Learning Engineer to join our team.The ideal candidate will have a strong background in Python and PyTorch, along with 4-8 years of experience deploying ML/AI models to production. This role requires excellent analytics skills and a good working knowledge of Databricks. You will work closely with data scientists, clinicians, software engineers, and product teams to design, build, and optimize scalable machine learning solutions. Role & responsibilities Develop, train, and optimize machine learning models using PyTorch and other ML frameworks. Deploy and maintain ML models in production environments, ensuring scalability, performance, and reliability. Utilize Databricks for data processing, model training, model deployment, and pipeline optimization. Deploy Retrieval-Augmented Generation (RAG) pipelines to production for improved AI-driven applications. Collaborate with data engineers to design and implement ETL workflows and data pipelines. Perform rigorous testing, validation, and monitoring of deployed models. Optimize model inference for low latency and high throughput applications. Work with stakeholders to translate business problems into ML solutions. Stay up to date with the latest advancements in machine learning, deep learning, and AI deployment strategies. Preferred candidate profile Proficiency in Python and ML frameworks such as PyTorch. 4-8 years of experience deploying machine learning models to production. Knowledge of MLflow for experiment tracking and model management. Strong experience with Databricks for ML development and deployment. Hands-on experience with MLOps, CI/CD pipelines, and cloud-based deployment (AWS, Azure, or GCP). Solid understanding of data structures, algorithms, and software engineering principles. Experience working with large-scale datasets and distributed computing frameworks. Experience with deploying Retrieval-Augmented Generation (RAG) pipelines to production. Excellent analytical and problem-solving skills. Strong communication skills and ability to work in a collaborative team environment. Preferred Qualifications Experience deploying models in the healthcare domain. Experience with feature engineering, data preprocessing, and model explainability. Knowledge of containerization (Docker, Kubernetes) and workflow orchestration tools Familiarity with LLMs, NLP, or reinforcement learning is a plus.

Role & responsibilities : Leadership & Strategy : Lead and manage a team of 20 Data Scientists (DS), ensuring high performance and continuous growth Define and implement the companys data science vision and strategy Work closely with global business leaders to identify opportunities where data science can drive business value Stay up to date with emerging trends in AI, ML, and advanced data analytics to drive innovation. Ensure all agreed Key Performance Indicators (KPIs) are met in line with performance agreements, maintaining high standards of operational efficiency Data Science : Develop and deploy machine learning models, statistical algorithms and predictive analytics solutions. Ensure the scalability, reliability and efficiency of data science models and pipelines. Collaborate with engineering and data teams to improve data quality, availability and infrastructure Participate in global meets & workshops to review & develop strong process and strategy for DS services in India Utilize advanced analytics to solve complex business problems and improve decision-making Stakeholder Management : Partner with cross-functional teams (engineering, operations) to translate business needs into data-driven solutions Communicate insights and recommendations to both technical and non-technical stakeholders Develop data-driven strategies to optimize customer experience, revenue and operational efficiency Exhibit very strong local and global stakeholders management skill Support in developing proactive proposals and solutions to internal and external stakeholders. Team Development & Mentorship : Recruit, train and mentor Data Scientists, Data Engineers and AI platform engineers, fostering a culture of innovation. Support HR partner with building hiring strategy for the niched skills Conduct performance evaluations, provide feedback and set career development plans for the team members Encourage best practices in coding, model deployment and data governance. Desired candidate profile : Bachelors or Masters or PhD in Data Science, Computer Science, Statistics, Mathematics or a related field. Technical Skills: 10-15 years of total experience with min 5 yrs in data science, analytics, AI/Deep Learning, NLP and Generative AI . Strong proficiency in Python, R, SQL and cloud platforms ( AWS, GCP, or Azure ) Expertise in Machine Learning frameworks ( TensorFlow, PyTorch, Scikit-learn ) Experience with big data technologies (Spark, Hadoop, Databricks) and data engineering concepts. Solid understanding of statistics, probability and optimization techniques. Experience with MLOps, model deployment and productionizing ML models . Industry experience in Aviation, Automotive, Finance, Healthcare, Retail or any IT Technology based domain. Leadership & Business Acumen : • Proven experience in leading and managing data science teams. • Ability to translate business problems into data science solutions. • Strong communication and stakeholder management skills. • Experience in budget planning, resource allocation and project management. • Proficiency in Risk assessment and mitigation planning.

AI Ops Senior Architect 12 -17 Years Work Location - Pune/ Bengaluru/Hyderabad/Chennai/ Gurugram Tredence is Data science, engineering, and analytics consulting company that partners with some of the leading global Retail, CPG, Industrial and Telecom companies. We deliver business impact by enabling last mile adoption of insights by uniting our strengths in business analytics, data science and data engineering. Headquartered in the San Francisco Bay Area, we partner with clients in US, Canada, and Europe. Bangalore is our largest Centre of Excellence with skilled analytics and technology teams serving our growing base of Fortune 500 clients. JOB DESCRIPTION At Tredence, you will lead the evolution of Industrializing AI ” solutions for our clients by implementing ML/LLM/GenAI & Agent Ops best practices. You will lead the Architecture , Design & development of large scale ML/LLMOps platforms for our clients. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll be a trusted advisor to our clients in ML/GenAI/Agent Ops space & coach to the ML engineering practitioners to build effective solutions to Industrialize AI solutions THE IDEAL CANDIDATE WILL BE RESPONSIBLE FOR AI Ops Strategy, Innovation, Research and Technical Standards 1. Conduct research and experiment with emerging AI Ops technologies and trends. Create POV’s, POC’s & present Proof of Technology to use latest tools, Technologies & services from Hyper scalers focussed on ML, GenAI & Agent Ops 2. Define and propose new technical standards and best practices for the organization's AI Ops environment. 3. Lead the evaluation and adoption of innovative MLOps solutions to address critical business challenges. 4. Conduct meet ups, attend & present in Industry events, conferences, etc 5. Ideate & develop accelerators to strengthen service offerings of AI Ops practice Solution Design & Architectural Development 6. Lead Design & architecture of scalable model training & deployment pipelines for large-scale deployments 7. Architect & Design large scale ML & GenAI Ops platforms 8. Collaborate with Data science & GenAI practice to define and implement strategies of AI solutions for model explainability and interpretability 9. Mentor and guide senior architects in crafting cutting-edge AI Ops solutions 10. Lead architecture reviews and identify opportunities for significant optimizations and improvements. Documentation and Best Practices 11. Develop and maintain comprehensive documentation of AIOps architectures designs and best practices. 12. Lead the development and delivery of training materials and workshops on AIOps tools and techniques. 13. Actively participate in sharing knowledge and expertise with the MLOps team through internal presentations and code reviews. Qualifications and Skills: 1. Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field with minimum 12 years of experience 2. Proven experience in architecting & developing AIOps solutions – to streamline Machine Learning & GenAI development lifecycle 3. Proven experience as an AI Ops Architect – ML & GenAI in architecting & design of ML & GenAI platforms 4. Hands on experience in Model deployment strategies, Designing ML & GenAI model pipelines to scale in production, Model Observability techniques used to monitor performance of ML & LLM’s 5. Strong coding skills with experience in implementing best coding practices Technical Skills & Expertise Python, PySpark, PyTorch ,Java, Micro Services, API’s LLMOps – Vector DB, RAG, LLM Orchestration tools, LLM Observability, LLM Guardrails, Responsible AI MLOps - MLFlow, ML/DL libraries, Model & Data Drift Detection libraries & techniques Real Time & Batch Streaming Container Orchestration Platforms Cloud platforms – Azure/ AWS/ GCP, Data Platforms – Databricks/ Snowflake Nice to Have: Understanding of Agent Ops Exposure to Databricks platform You can expect to – Work with world’s biggest Retailers, CPG’s, HealthCare, Banking & Manufacturing customers and help them solve some of their most critical problems Create multi-million Dollar business opportunities by leveraging impact mindset, cutting edge solutions and industry best practices. Work in a diverse environment that keeps evolving Hone your entrepreneurial skills as you contribute to growth of the organization