66 Mlops Tools Jobs - Page 3

DecisionX is pioneering a new category with the world's first Decision AI, an AI Super-Agent that assists high-growth teams in making smarter, faster decisions by transforming fragmented data into clear next steps. Whether it involves strategic decisions in the boardroom or operational decisions across various departments like Sales, Marketing, Product, and Engineering, down to the minutiae that drives daily operations, Decision AI serves as your invisible co-pilot, thinking alongside you, acting ahead of you, and evolving beyond you. We are seeking a dedicated and hands-on AI Engineer to join our Founding team. In this role, you will collaborate closely with leading AI experts to develop the intelligence layer of our exclusive "Agentic Number System." Key Responsibilities - Building, fine-tuning, and deploying AI/ML models for tasks such as segmentation, scoring, recommendation, and orchestration. - Developing and optimizing agent workflows using LLMs (OpenAI, Claude, Mistral, etc.) for contextual reasoning and task execution. - Creating vector-based memory systems utilizing tools like FAISS, Chroma, or Weaviate. - Working with APIs and connectors to incorporate third-party data sources (e.g., Salesforce, HubSpot, GSuite, Snowflake). - Designing pipelines that transform structured and unstructured signals into actionable insights. - Collaborating with GTM and product teams to define practical AI agent use cases. - Staying informed about the latest developments in LLMs, retrieval-augmented generation (RAG), and agent orchestration frameworks (e.g., CrewAI, AutoGen, LangGraph). Must Have Skills - 5-8 years of experience in AI/ML engineering or applied data science. - Proficient programming skills in Python, with expertise in LangChain, Pandas, NumPy, and Scikit-learn. - Experience with LLMs (OpenAI, Anthropic, etc.), prompt engineering, and RAG pipelines. - Familiarity with vector stores, embeddings, and semantic search. - Expertise in data wrangling, feature engineering, and model deployment. - Knowledge of MLOps tools such as MLflow, Weights & Biases, or equivalent. What you will get - Opportunity to shape the AI architecture of a high-ambition startup. - Close collaboration with a visionary founder and experienced product team. - Ownership, autonomy, and the thrill of building something from 0 to 1. - Early team equity and a fast growth trajectory.,

Yubi, formerly known as CredAvenue, is re-defining global debt markets by freeing the flow of finance between borrowers, lenders, and investors. We are the world's possibility platform for the discovery, investment, fulfillment, and collection of any debt solution. At Yubi, opportunities are plenty and we equip you with tools to seize it. In March 2022, we became India's fastest fintech and most impactful startup to join the unicorn club with a Series B fundraising round of $137 million. In 2020, we began our journey with a vision of transforming and deepening the global institutional debt market through technology. Our two-sided debt marketplace helps institutional and HNI investors find the widest network of corporate borrowers and debt products on one side and helps corporates to discover investors and access debt capital efficiently on the other side. Switching between platforms is easy, which means investors can lend, invest and trade bonds - all in one place. All of our platforms shake up the traditional debt ecosystem and offer new ways of digital finance. Yubi Credit Marketplace - With the largest selection of lenders on one platform, our credit marketplace helps enterprises partner with lenders of their choice for any and all capital requirements. Yubi Invest - Fixed income securities platform for wealth managers & financial advisors to channel client investments in fixed income Financial Services Platform - Designed for financial institutions to manage co-lending partnerships & asset-based securitization Spocto - Debt recovery & risk mitigation platform Corpository - Dedicated SaaS solutions platform powered by Decision-grade data, Analytics, Pattern Identifications, Early Warning Signals, and Predictions to Lenders, Investors, and Business Enterprises So far, we have onboarded over 17000+ enterprises, 6200+ investors & lenders and have facilitated debt volumes of over INR 1,40,000 crore. Backed by marquee investors like Insight Partners, B Capital Group, Dragoneer, Sequoia Capital, LightSpeed, and Lightrock, we are the only-of-its-kind debt platform globally, revolutionizing the segment. At Yubi, People are at the core of the business and our most valuable assets. Yubi is constantly growing, with 1000+ like-minded individuals today, who are changing the way people perceive debt. We are a fun bunch who are highly motivated and driven to create a purposeful impact. Come, join the club to be a part of our epic growth story. This particular role is within our Yubi Invest vertical, and you would get to work on building our bonds platform, called Aspero, for retail users. Be able to operate in ambiguous situations and define clear objectives by breaking down the narratives independently. Work closely with business, research, data and engineering teams to understand the user goals, market dynamics and ship products. Aligning product strategy, proposition and roadmap with measurable metrics with all stakeholders. Drive PRDs, product planning, and product design of new features and enhancements. Clearly communicate product and platform benefits to our users and internal stakeholders. We're looking for a highly skilled, results-driven AI engineer who thrives in fast-paced, high-impact environments. If you are passionate about pushing the boundaries of Computer Vision, OCR, and Large Language Models (LLMs) and have a strong foundation in building and deploying AI solutions, this role is for you. As a Senior Data Scientist, you will take ownership of designing and implementing state-of-the-art OCR and Computer Vision systems. This role demands deep technical expertise, the ability to work autonomously, and a mindset that embraces complex challenges head-on. Here, you won't just fine-tune pre-trained modelsyou'll be architecting, optimizing, and scaling AI solutions that power real-world applications. Key Responsibilities: - Architect, develop, and deploy high-performance Computer Vision and OCR models for real-world applications. - Implement and optimize state-of-the-art OCR models such as Donut, TrOCR, LayoutLM, and DocFormer for document processing and information extraction. - Fine-tune and integrate LLMs (GPT, LLaMA, Mistral, etc.) to enhance text understanding and automation. - Develop custom deep learning models for large-scale image and document processing. - Build and optimize end-to-end AI pipelines, ensuring efficient data processing and model deployment. - Work closely with engineers to operationalize AI models in production (Docker, FastAPI, TensorRT, ONNX). - Enhance GPU performance and model inference efficiency, applying techniques such as quantization and pruning. - Stay ahead of industry advancements, continuously experimenting with new AI architectures and training techniques. - Work in a highly dynamic, startup-like environment, balancing rapid experimentation with production-grade robustness. Requirements: - 5-10 years experience - Proven technical expertise - Strong programming skills in Python, PyTorch, TensorFlow with deep experience in Computer Vision and OCR. - Hands-on experience in developing, training, and deploying OCR and document AI models. - Deep understanding of Transformer-based architectures for vision and text processing. - Experience working with Hugging Face, OpenCV, TensorRT, and NVIDIA GPUs for model acceleration. - Autonomous problem solver - Strong experience in scaling AI solutions, including model optimization and deployment on cloud platforms (AWS/GCP/Azure). - Thrives in fast-paced environments - Familiarity with MLOps tools (Docker, FastAPI, Kubernetes) for seamless model deployment. - Experience in multi-modal models (Vision + Text). Nice to Have: - Strong background in vector databases, RAG pipelines, and fine-tuning LLMs for document intelligence. - Contributions to open-source AI projects.,

As a GEN AI and Machine Learning Engineer, you will be responsible for leveraging your Bachelor's or Masters degree in Computer Science, Data Science, Engineering, or a related field to develop and deploy machine learning and AI models. With a solid foundation in mathematics, statistics, and probability, you will utilize your strong programming skills in Python and proficiency in SQL/NoSQL databases to build analytical approaches based on business requirements. Your hands-on experience with Agentic AI frameworks will be crucial in building AI-driven features and solutions by collaborating with software engineers, business stakeholders, and domain experts. You will have the opportunity to work with Generative AI models and tools like OpenAI, Google Gemini, and Runway ML, in addition to AI/ML and deep learning frameworks such as TensorFlow, PyTorch, Scikit-learn, OpenCV, and Keras. In this role, you will preprocess and analyze large-scale datasets to extract meaningful insights, identify trends, and develop RESTful APIs using Flask or Django. Proficiency in cloud environments like AWS, Azure, or GCP will be essential as you deploy AI/ML applications and continuously monitor model performance for accuracy, scalability, and generalization. Additionally, your familiarity with ML operations tools such as MLFlow, Kubeflow, and CI/CD pipelines will be advantageous in evaluating and optimizing model performance. Experience with Docker, Kubernetes, and container orchestration, as well as frontend technologies like HTML, CSS, JavaScript/jQuery, Node.js, Angular, or React, will be beneficial for this role. Overall, as a GEN AI and Machine Learning Engineer, you will play a key role in designing, developing, and deploying AI/ML, NLP/NLU, and deep learning models and applications. Your ability to document development processes, results, and best practices will contribute to transparency and continuous learning within the organization.,

The Senior AI Consultant role at G+D involves enabling RAG-based use cases through fine-tuning and prompt engineering expertise. You will be responsible for translating business requirements into actionable RAG solutions, designing tailored workflows, and driving user training to ensure optimal performance and scalability of AI applications. Your key responsibilities will include interpreting business needs for structured RAG use cases, advising on RAG workflows, configuring the SaaS platform, optimizing LLMs for chatbots and text analytics, and leading user training sessions for continuous improvement. Required skills for this role include a strong background in AI/ML with hands-on experience in generative AI and RAG methodologies, working knowledge of Agentic AI & MCP concepts, proficiency in Python and familiarity with frameworks like TensorFlow and PyTorch, experience with cloud platforms (preferably Azure) and MLOps tools such as Docker, Kubernetes, and CI/CD, as well as excellent communication and consultative skills to bridge tech and business needs. Preferred qualifications for the Senior AI Consultant position include proven production-grade deployment of RAG solutions, experience with large-scale datasets and vector databases like FAISS and Milvus, and a solid understanding of AI ethics and responsible AI practices. Join us at G+D, a global technology group with over 14,000 employees in 40 countries, where creating confidence is our path to success and trust is the foundation of our cooperation. Let's work together to make the lives of billions of people around the world more secure through innovative solutions in Digital Security, Financial Platforms, and Currency Technology.,

You will be responsible for designing and developing scalable AI and machine learning models to address complex business challenges. Your role will involve leading end-to-end model development, including data collection, preprocessing, training, validation, deployment, and monitoring. Collaboration with cross-functional teams, such as data engineers, software developers, and product managers, will be essential. Additionally, you will optimize existing AI models for enhanced performance and accuracy. To excel in this role, you should stay abreast of the latest research and trends in AI/ML and assess their relevance to company products. Documenting processes, models, and code for knowledge sharing and reproducibility purposes will also be part of your responsibilities. Furthermore, mentoring junior team members and fostering a culture of innovation will be key components of your role. The ideal candidate for this position should possess a Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. You should have at least 5 years of experience in AI/ML, data science, or related roles. Proficiency in Python and ML frameworks like TensorFlow, PyTorch, and scikit-learn is essential. A solid grasp of machine learning algorithms, data structures, and software engineering principles is also required. Experience with cloud platforms such as AWS, Azure, or GCP, as well as familiarity with MLOps tools, will be advantageous. Hands-on experience in NLP, computer vision, or deep learning applications is desirable. Strong problem-solving and analytical skills, coupled with excellent communication and collaboration abilities, are crucial for success in this role. While a PhD in an AI/ML-related field is not mandatory, it would be a significant asset. Experience in deploying models in production environments using CI/CD pipelines, as well as contributions to open-source AI/ML projects or publications in esteemed journals or conferences, will be considered advantageous for this position.,

As a Data Science/Machine Learning Instructor at our organization, you will be responsible for conducting live instructions to educate working professionals on core topics related to Data Science and Machine Learning. This includes topics such as supervised and unsupervised learning, deep learning, model evaluation, and MLOps through engaging and interactive sessions. You will collaborate with content teams to develop labs, code walkthroughs, and real-world case studies using Python, scikit-learn, TensorFlow/PyTorch, and cloud-based Data Science/Machine Learning services. Your role will also involve providing support to learners by addressing technical queries, debugging code, reviewing notebooks, and offering constructive feedback on assignments and projects. Additionally, you will mentor learners on capstone projects such as image/video models, NLP pipelines, recommendation systems, and deployment pipelines. Continuous improvement is a key aspect of your responsibilities, which includes analyzing learner performance data to enhance modules, introduce new topics like transformers and generative models, and improve assessments. To qualify for this position, you should have a minimum of 3 years of industry or academic experience in Data Science, Machine Learning, or Artificial Intelligence. A Master's degree in Data Science, Machine Learning, Artificial Intelligence, or a Computer Science specialization in AI and Data Science/Machine Learning is required. Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch is essential. Familiarity with MLOps tools like Docker, Kubernetes, MLflow, and cloud ML services such as AWS SageMaker, GCP AI Platform, or Azure ML is preferred. Strong presentation and mentoring skills in live and small-group settings are necessary for this role. Prior experience in teaching or educational technology would be advantageous. If you are passionate about Data Science and Machine Learning and possess the required qualifications and skills, we invite you to join our team as a Data Science/Machine Learning Instructor.,

We are seeking a skilled and forward-thinking Lead Software Engineer specializing in Machine Learning with over 10 years of experience to spearhead the conceptualization, development, and implementation of cutting-edge machine learning solutions. This pivotal role necessitates robust leadership qualities, profound technical acumen, and a demonstrated track record in steering teams towards resolving intricate, large-scale challenges utilizing state-of-the-art ML technologies. In this leadership capacity, you will be responsible for mentoring teams, formulating technical roadmaps, and fostering collaboration across various departments to synchronize machine learning endeavors with business objectives. Your responsibilities will include defining and orchestrating the strategy and trajectory for ML systems and applications, as well as architecting and supervising the construction of adaptable machine learning systems and infrastructure. You will drive the creation and execution of sophisticated ML models and algorithms to tackle complex business issues, collaborate with multifaceted teams to discern ML use cases and prerequisites, and provide guidance to junior and mid-level engineers on optimal practices for ML development and deployment. It will also be imperative to oversee the performance enhancement of machine learning systems in operational settings, ensure adherence to industry standards and best practices in model development, data governance, and MLOps, and spearhead research endeavors to explore emerging ML methodologies and seamlessly integrate them into the organization's solutions. The ideal candidate should hold a Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field, with a Ph.D. being an advantageous asset. Additionally, you should possess a minimum of 10 years of software engineering experience, with at least 5 years dedicated to machine learning, and exhibit proficiency in ML frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. A strong grasp of designing and constructing large-scale, distributed ML systems, advanced knowledge of data engineering tools and frameworks such as Spark, Hadoop, or Kafka, hands-on experience with cloud platforms (AWS, GCP, Azure) for ML workloads, and expertise in deploying and managing ML models in production environments using MLOps tools like MLflow or Kubeflow are essential technical skills that you should bring to the table. Moreover, a deep understanding of algorithms, data structures, system design, containerization (Docker), orchestration (Kubernetes), and exceptional problem-solving capabilities are highly valued attributes for this role. Your soft skills should include robust leadership and decision-making prowess, exceptional problem-solving and analytical thinking, excellent communication aptitude to convey technical concepts to diverse audiences, and the ability to cultivate collaboration and drive innovation across teams. Preferred qualifications include a Master's degree in Computer Science, Information Technology, or a related field, familiarity with advanced techniques like generative AI, reinforcement learning, or federated learning, experience in constructing and managing real-time data processing pipelines, knowledge of data security and privacy best practices, and a track record of publications or patents in the domain of machine learning or artificial intelligence. Key Performance Indicators for this role encompass the successful delivery of scalable, high-impact ML solutions in alignment with business objectives, effective mentorship and upskilling of team members, continuous enhancement of ML system performance and reliability, and driving innovation and adoption of emerging ML techniques to sustain a competitive advantage.,

You are a Data Science Engineer who will be contributing to the development of intelligent, autonomous AI systems. The ideal candidate should have a strong background in agentic AI, LLMs, SLMs, vector DB, and knowledge graphs. Your responsibilities will include deploying AI solutions that leverage technologies such as Retrieval-Augmented Generation (RAG), multi-agent frameworks, and hybrid search techniques to enhance enterprise applications. As part of the flexible scheme, you will enjoy various benefits such as a best-in-class leave policy, gender-neutral parental leaves, childcare assistance benefit reimbursement, sponsorship for industry-relevant certifications, employee assistance program, comprehensive hospitalization insurance, accident and term life insurance, and health screening. Your key responsibilities will involve designing and developing Agentic AI Applications using frameworks like LangChain, CrewAI, and AutoGen, implementing RAG Pipelines, fine-tuning Language Models, training NER Models, developing Knowledge Graphs, collaborating cross-functionally, and optimizing AI workflows. To excel in this role, you should have at least 4 years of professional experience in AI/ML development, proficiency in Python, Python API frameworks, SQL, and familiarity with AI/ML frameworks like TensorFlow or PyTorch. Experience in deploying AI models on cloud platforms, understanding of LLMs, SLMs, semantic technologies, and MLOps tools is required. Additionally, hands-on experience with vector databases, embedding techniques, and developing AI solutions for specific industries will be beneficial. You will receive support through training, coaching, and a culture of continuous learning to aid in your career progression. The company strives for a culture of empowerment, responsibility, commercial thinking, initiative, and collaboration. They promote a positive, fair, and inclusive work environment for all individuals. For further information about the company and its teams, please visit the company website at https://www.db.com/company/company.htm. Join a team that celebrates success and fosters a culture of excellence and inclusivity.,

Job Summary: We are seeking a highly skilled and motivated Machine Learning Engineer with a strong foundation in programming and machine learning, hands-on experience with AWS Machine Learning services (especially SageMaker), and a solid understanding of Data Engineering and MLOps practices. You will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment. Key Responsibilities: • Design and implement machine learning models and pipelines using AWS SageMaker and related services. • Develop and maintain robust data pipelines for training and inference workflows. • Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions. • Implement MLOps best practices including CI/CD for ML, model versioning, monitoring, and retraining strategies. • Optimize model performance and ensure scalability and reliability in production environments. • Monitor deployed models for drift, performance degradation, and anomalies. • Document processes, architectures, and workflows for reproducibility and compliance. Required Skills & Qualifications: • Strong programming skills in Python and familiarity with ML libraries (e.g., scikitlearn, TensorFlow, PyTorch). • Solid understanding of machine learning algorithms, model evaluation, and tuning. • Hands-on experience with AWS ML services, especially SageMaker, S3, Lambda, Step Functions, and CloudWatch. • Experience with data engineering tools (e.g., Apache Airflow, Spark, Glue) and workflow orchestration. Machine Learning Engineer - Job Description • Proficiency in MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes). • Familiarity with monitoring tools and logging frameworks for ML systems. • Excellent problem-solving and communication skills. Preferred Qualifications: • AWS Certification (e.g., AWS Certified Machine Learning Specialty). • Experience with real-time inference and streaming data. • Knowledge of data governance, security, and compliance in ML systems

The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key Requirements of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets. Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference. Should have at least a foundational understanding in Experimentation design ? Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting. Should be proficient in developing production ready code as per best practices. Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL. Proficient is using NoSQL databases. Experience in using GraphDBs like Neo4j a plus. Candidate should be able to handle unstructured data with ease. Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools. Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle. Experience in CI/CD deployment is a big plus. Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc. a big plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills ? Should be able to guide and mentor teams while leading them by example. Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams ? Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space ? Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical job role: 1. As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions 2. Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem 3. Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development 4. Build re-usable code analytical frameworks to address commonly occurring business questions 5. Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty 6. Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges 7. The role will be an approximate 60% IC 40% leading and the ratios can vary basis need and fit 8. Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer

AI/ML Architect Experience 10+ years in total, 8+ years in AI/ML development 3+ years in AI/ML architecture Education Bachelors/Masters in CS, AI/ML, Engineering, or similar Title: AI/ML Architect Location: Onsite Bangalore Experience: 10+ years Position Summary: We are seeking an experienced AI/ML Architect to lead the design and deployment of scalable AI solutions. This role requires a strong blend of technical depth, systems thinking, and leadership in machine learning , computer vision , and real-time analytics . You will drive the architecture for edge, on-prem, and cloud-based AI systems, integrating 3rd party data sources, sensor and vision data to enable predictive, prescriptive, and autonomous operations across industrial environments. Key Responsibilities: Architecture & Strategy Define the end-to-end architecture for AI/ML systems including time series forecasting , computer vision , and real-time classification . Design scalable ML pipelines (training, validation, deployment, retraining) using MLOps best practices. Architect hybrid deployment models supporting both cloud and edge inference for low-latency processing. Model Integration Guide the integration of ML models into the IIoT platform for real-time insights, alerting, and decision support. Support model fusion strategies combining disparate data sources, sensor streams with visual data (e.g., object detection + telemetry + 3rd party data ingestion). MLOps & Engineering Define and implement ML lifecycle tooling, including version control, CI/CD, experiment tracking, and drift detection. Ensure compliance, security, and auditability of deployed ML models. Collaboration & Leadership Collaborate with Data Scientists, ML Engineers, DevOps, Platform, and Product teams to align AI efforts with business goals. Mentor engineering and data teams in AI system design, optimization, and deployment strategies. Stay ahead of AI research and industrial best practices; evaluate and recommend emerging technologies (e.g., LLMs, vision transformers, foundation models). Must-Have Qualifications: Bachelors or Master’s degree in Computer Science, AI/ML, Engineering, or a related technical field. 8+ years of experience in AI/ML development, with 3+ years in architecting AI solutions at scale. Deep understanding of ML frameworks (TensorFlow, PyTorch), time series modeling, and computer vision. Proven experience with object detection, facial recognition, intrusion detection , and anomaly detection in video or sensor environments. Experience in MLOps (MLflow, TFX, Kubeflow, SageMaker, etc.) and model deployment on Kubernetes/Docker . Proficiency in edge AI (Jetson, Coral TPU, OpenVINO) and cloud platforms (AWS, Azure, GCP). Nice-to-Have Skills: Knowledge of stream processing (Kafka, Spark Streaming, Flink). Familiarity with OT systems and IIoT protocols (MQTT, OPC-UA). Understanding of regulatory and safety compliance in AI/vision for industrial settings. Experience with charts, dashboards, and integrating AI with front-end systems (e.g., alerts, maps, command center UIs). Role Impact: As AI/ML Architect, you will shape the intelligence layer of our IIoT platform — enabling smarter, safer, and more efficient industrial operations through AI. You will bridge research and real-world impact , ensuring our AI stack is scalable, explainable, and production-grade from day one.

Role: MLOps Engineer Experience : 8 yr to 15 yr Location: PAN India Key words -Skillset AWS SageMaker, Azure ML Studio, GCP Vertex AI PySpark, Azure Databricks MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline Kubernetes, AKS, Terraform, Fast API Responsibilities: Model Deployment, Model Monitoring, Model Retraining Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline Drift Detection, Data Drift, Model Drift Experiment Tracking MLOps Architecture REST API publishing Job Responsibilities: Research and implement MLOps tools, frameworks and platforms for our Data Science projects. Work on a backlog of activities to raise MLOps maturity in the organization. Proactively introduce a modern, agile and automated approach to Data Science. Conduct internal training and presentations about MLOps tools benefits and usage. Required experience and qualifications: Wide experience with Kubernetes. Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube). Good understanding of ML and AI concepts. Hands-on experience in ML model development. Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit. Experience in CI/CD/CT pipelines implementation. Experience with cloud platforms - preferably AWS - would be an advantage.

Job Overview: We are seeking a highly skilled and experienced Generative AI Expert with deep expertise in AWS to lead the development and deployment of cutting-edge generative AI solutions. The ideal candidate will have a strong background in machine learning, deep learning (particularly transformer-based models), and cloud-native development on AWS. Required Skills and Qualifications: Must have proven experience in building and deploying generative AI models (such as LLMs, GANs, diffusion models). Must have deep expertise in AWS AI/ML stack : SageMaker, Bedrock, EC2, Lambda, S3, CloudWatch, IAM, etc. Must have strong programming skills in Python (TensorFlow, PyTorch, Transformers, LangChain, etc.). Must have experience with MLOps tools (e.g., SageMaker Pipelines, MLflow, Kubeflow). Must be familiar with Retrieval-Augmented Generation (RAG) using AWS-native services and frameworks. Must have strong understanding of AWS architecture , networking, and security best practices. Must have experience in creating a RAG system for document search using Amazon Kendra and LangChain . Must have experience in building a secure, scalable chatbot using a fine-tuned LLM on AWS . MUST HAVE AWS Certified Machine Learning Specialty , Solutions Architect Professional Certification(s). Excellent communication and documentation skills. Experience with Amazon Bedrock, AWS Inferentia, and AWS Trainium would be a plus Prior experience with multi-modal models (e.g., image + text) and use cases like synthetic media generation would be a plus. Must have background in NLP, CV, or RL research would be a plus. Must have experience with CI/CD for ML (e.g., CodePipeline, CodeBuild). Prior experience in developing generative image or video pipelines for marketing using AWS infrastructure. Prior experience in integrating third-party LLM APIs via Bedrock and deploying with custom prompt engineering pipelines. Key Responsibilities: Design, develop, and deploy generative AI models (e.g., LLMs, diffusion models) using AWS services . Architect scalable, secure, and cost-effective ML infrastructure on AWS (e.g., Sagemaker , Lambda, ECS, EKS). Fine-tune foundation models (e.g., LLaMA, Falcon, Claude, Mistral) using Amazon SageMaker or custom pipelines. Collaborate with data scientists, ML engineers, and DevOps to operationalize AI models into production. Implement real-time inference pipelines using AWS services such as SageMaker Endpoints, Lambda, and API Gateway. Evaluate and integrate third-party APIs (e.g., Bedrock, OpenAI, Anthropic) with custom workflows. Optimize model training and inference performance using GPU-based instances (e.g., EC2 P4d, G5) and distributed training. Ensure compliance with security, privacy, and governance policies when deploying models in AWS environments. Stay up to date with the latest in generative AI research, tools, and AWS innovations.

Mid-Level AI/ML Engr-design, develop, deploy ML model & AI solution, with focus on production-ready system.Collaborate closely with data scientists,software engineer, product manager to deliver AI capabilities that directly impact customer & business Perks and benefits Health insurance and wellness programs

Job Title: AI Engineer Location: Onsite. Sangamner, Maharashtra About the Role: We are looking for a passionate and experienced AI Engineer to join our growing team. In this role, you will develop and deploy advanced AI and machine learning models that power our next-generation products and services. You'll collaborate with cross-functional teams to solve challenging problems, from data preprocessing and model design to deployment and optimization. Responsibilities: Design, build, and deploy machine learning and deep learning models. Collaborate with data scientists, product managers, and engineers to implement AI solutions into production. Work with large datasets; perform data cleaning, preprocessing, and feature engineering. Evaluate model performance using appropriate metrics and continuously improve model accuracy and efficiency. Stay current with the latest research and advancements in AI/ML technologies and tools. Build scalable and maintainable ML pipelines and APIs. Ensure ethical AI practices and model explainability where necessary. Required Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. 3+ years of experience in AI/ML engineering, data science, or a related role. Proficiency in Python and common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Strong understanding of machine learning algorithms, deep learning architectures (CNNs, RNNs, Transformers), and NLP techniques. Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools is a plus. Familiarity with version control systems (e.g., Git) and CI/CD pipelines. Strong problem-solving skills and ability to work in a fast-paced environment. Preferred Skills (Nice to Have): Experience with real-time inference systems or edge AI. Knowledge of data labeling tools and techniques. Exposure to reinforcement learning or generative models. Contributions to open-source projects or published papers.

Role & Responsibilities Conversational AI & Chatbot Development Design and implement intelligent chatbot solutions using modern LLMs like GPT-4, Claude, LLaMA, etc. Build prompt strategies, chains, and RAG (Retrieval-Augmented Generation) pipelines for enhanced context-aware responses. Integrate chatbot systems with APIs, databases, and enterprise applications. Optimize model usage to balance cost, latency, and accuracy. Develop fallback logic, session tracking, and context management for multi-turn conversations. Ensure secure, ethical, and responsible AI usage. AI/ML for Predictive Analytics Analyze historical and real-time data to identify trends, anomalies, and patterns. Work with structured and unstructured data including text, time series, and tabular data. Present actionable insights from models to stakeholders in clear and explainable ways. Architecture & Deployment Design and maintain scalable AI/ML pipelines and architecture. Use MLOps tools to automate model training, testing, deployment, and monitoring (e.g., MLflow, DVC, Airflow, Kubeflow). Integrate models into production-grade applications using APIs and cloud ML services. Qualifications 2-4 years of relevant experience in AI Engineering. Strong experience with LLM-based chatbot frameworks: LangChain, LlamaIndex, Langgraph, etc. Expertise in prompt engineering, embeddings, and transformer-based models. Proficiency in Python (preferred) or other languages for ML and scripting. Familiarity with data pipelines, feature stores, and ETL processes. Experience with vector databases (e.g., Pinecone, FAISS, Weaviate). Strong understanding of machine learning algorithms, statistics, and data modeling. Education- Bachelors/Masters degree in Computer Science, Data Science, AI/ML