169 Kubeflow Jobs - Page 4

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

Job Overview The Applied AI Solutions Architect is a strategic role responsible for designing, implementing, and managing AI-driven solutions that align with business objectives. This position bridges technical expertise in artificial intelligence (AI) and machine learning (ML) with business strategy, ensuring scalable, ethical, and high-performing AI systems. The AI Solutions Architect collaborates with cross-functional teams to deliver innovative solutions, leveraging generative AI, cloud platforms, and modern architectures like Retrieval-Augmented Generation (RAG). Responsibilities Solution Design : Architect end-to-end AI/ML pipelines, including data ingestion, preprocessing, model training, deployment, and monitoring, ensuring scalability and performance. Technology Selection : Evaluate and select appropriate AI frameworks, tools, and cloud services (e.g., AWS SageMaker, Azure AI, Google Cloud AI) based on project requirements. Generative AI Implementation : Design solutions using large language models (LLMs) and RAG architectures for applications like content generation, customer engagement, or product design. Collaboration : Work with data scientists, engineers, product managers, and executives to translate business needs into technical solutions, acting as a trusted advisor. Governance and Ethics : Implement responsible AI practices, addressing bias, security, and compliance in AI systems. MLOps and AIOps : Establish CI/CD pipelines, model versioning, and monitoring frameworks to operationalize AI solutions. Thought Leadership : Advocate for AI-driven innovation, mentor teams, and communicate technical concepts to non-technical stakeholders. Performance Optimization : Ensure AI solutions meet latency, cost, and quality requirements, optimizing for production environments. Skill Set Technical Skills : Proficiency in Python, R, or Julia for AI/ML development. Expertise in ML frameworks like TensorFlow, PyTorch, Hugging Face, or Scikit-learn. Experience with cloud platforms (AWS, Azure, Google Cloud) and AI services like Amazon Bedrock or Azure AI Foundry. Knowledge of MLOps tools (e.g., Kubeflow, MLflow) and CI/CD pipelines. Familiarity with generative AI techniques, including prompt engineering, fine-tuning, and RAG. Understanding of data engineering concepts, including ETL processes and data lakes. Soft Skills : Strong communication to bridge technical and business teams. Analytical thinking for evaluating trade-offs and designing optimal solutions. Leadership and mentorship to guide cross-functional teams. Domain Knowledge : Experience in industries like healthcare, finance, or technology, with an understanding of relevant use cases (e.g., drug discovery, personalized marketing). Certifications AWS Certified Machine Learning Specialty Microsoft Azure AI Engineer Associate Google Cloud Professional Machine Learning Engineer Coursera or Edureka AI/ML certifications (e.g., DeepLearning.AIs Generative AI Specialization) ITIL or TOGAF for enterprise architecture alignment (optional) Qualifications Bachelors or Masters degree in Computer Science, Data Science, or a related field. 8+ years of experience in ML engineering, data science, or software architecture, with 3+ years in AI/ML solution design. Proven track record of deploying AI solutions in production environments

You will be joining a renowned consulting firm known for being consistently ranked as one of the world's best places to work. The company has maintained a top position on Glassdoor's Best Places to Work list since 2009, emphasizing the importance of extraordinary teams in their business strategy. By intentionally bringing together diverse backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment, they ensure that every individual can thrive both professionally and personally. As part of the Application Engineering experts team within the AI, Insights & Solutions division, you will collaborate with a multidisciplinary group of professionals including analytics, engineering, product management, and design experts. Your role will involve leveraging deep technical expertise along with business acumen to assist clients in addressing their most transformative challenges. Working in integrated teams, you will develop data-driven strategies and innovative solutions to drive competitive advantage for clients by harnessing the power of data and artificial intelligence. Your responsibilities will include designing, developing, and maintaining cloud-based AI applications using a full-stack technology stack to deliver high-quality, scalable, and secure solutions. You will collaborate with cross-functional teams to define and implement analytics features, utilize Kubernetes and containerization technologies for deployment, develop APIs and microservices, ensure robust security measures, monitor application performance, contribute to coding standards, stay updated on emerging technologies, automate deployment processes, and collaborate closely with clients to assess opportunities and develop analytics solutions. To qualify for this position, you are required to have a Master's degree in Computer Science, Engineering, or a related technical field, along with at least 6 years of experience at a Senior or Staff level. Proficiency in client-side and server-side technologies, cloud platforms, Python, Git, DevOps, CI/CD, and various other technical skills is necessary. Additionally, strong interpersonal and communication skills, curiosity, proactivity, critical thinking, and a solid foundation in computer science fundamentals are essential for this role. This role also requires a willingness to travel up to 30% of the time. If you are looking for an opportunity to work in a collaborative and supportive environment, continuously learn and grow, and contribute to developing cutting-edge analytics solutions for clients across different sectors, this position may be the perfect fit for you.,

As a Software Engineer - Backend (Python) with 7+ years of experience, you will be responsible for designing and building the backend components of the GenAI Platform in Hyderabad. Your role will involve collaborating with geographically distributed cross-functional teams and participating in an on-call rotation to handle production incidents. The GenAI Platform offers safe, compliant, and cost-efficient access to LLMs, including Opensource & Commercial ones, while adhering to Experian standards and policies. You will work on building reusable tools, frameworks, and coding patterns for fine-tuning LLMs or developing RAG-based applications. To succeed in this role, you must possess the following skills: - 7+ years of professional backend web development experience with Python - Experience with AI and RAG - Proficiency in DevOps & IaC tools like Terraform, Jenkins - Familiarity with MLOps platforms such as AWS Sagemaker, Kubeflow, or MLflow - Expertise in web development frameworks such as Flask, Django, or FastAPI - Knowledge of concurrent programming designs like AsyncIO - Experience with public cloud platforms like AWS, Azure, GCP (preferably AWS) - Understanding of CI/CD practices, tools, and frameworks Additionally, the following skills would be considered nice to have: - Experience with Apache Kafka and developing Kafka client applications in Python - Familiarity with big data processing frameworks, especially Apache Spark - Knowledge of containers (Docker) and container platforms like AWS ECS or AWS EKS - Proficiency in unit and functional testing frameworks - Experience with various Python packaging options such as Wheel, PEX, or Conda - Understanding of metaprogramming techniques in Python Join our team and contribute to the development of cutting-edge technologies in a collaborative and dynamic environment.,

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.,

As a Machine Learning Engineer, you will be responsible for designing and implementing scalable machine learning models throughout the entire lifecycle - from data preprocessing to deployment. Your role will involve leading feature engineering and model optimization efforts to enhance performance and accuracy. Additionally, you will build and manage end-to-end ML pipelines using MLOps practices, ensuring seamless deployment, monitoring, and maintenance of models in production environments. Collaboration with data scientists and product teams will be key in understanding business requirements and translating them into effective ML solutions. You will conduct advanced data analysis, create visualization dashboards for insights, and maintain detailed documentation of models, experiments, and workflows. Moreover, mentoring junior team members on best practices and technical skills will be part of your responsibilities to foster growth within the team. In terms of required skills, you must have at least 3 years of experience in machine learning development, with a focus on the end-to-end model lifecycle. Proficiency in Python using Pandas, NumPy, and Scikit-learn for advanced data handling and feature engineering is crucial. Strong hands-on expertise in TensorFlow or PyTorch for deep learning model development is also a must-have. Desirable skills include experience with MLOps tools like MLflow or Kubeflow for model management and deployment, familiarity with big data frameworks such as Spark or Dask, and exposure to cloud ML services like AWS SageMaker or GCP AI Platform. Additionally, working knowledge of Weights & Biases and DVC for experiment tracking and versioning, as well as experience with Ray or BentoML for distributed training and model serving, will be considered advantageous. Join our team and contribute to cutting-edge machine learning projects while continuously improving your skills and expertise in a collaborative and innovative environment.,

Major Tasks of Position: Design, develop and maintain UI applications and deploying/managing machine learning models. Design, build, and maintain Python and Flask applications using AWS and/or Azure cloud services. Implement standards for data ingestion, storage, and processing to support analytics and machine learning workflows. Understand graph theory and network analysis, including familiarity with libraries such as NetworkX, igraph, or similar. Implement and manage CI/CD pipelines for automated testing, deployment, and monitoring of machine learning models. Collaborate with data scientists, machine learning engineers, and software developers to operationalize machine learning models. Design and maintain infrastructure for automated deployment and scaling. Ensure compliance with security, privacy, and data governance requirements. Key Working Relation: Qualification & Competencies: Bachelors degree in computer science, engineering, or a related field, or equivalent practical experience with at least 8-10 years of combined experience as a Python and MLOps Engineer or similar roles. Strong programming skills in Python. Proficiency with AWS and/or Azure cloud platforms, including services such as EC2, S3, Lambda, SageMaker, Azure ML, etc. Solid understanding of API programming and integration. Hands-on experience with CI/CD pipelines, version control systems (e.g., git), and code repositories. Knowledge of containerization using Docker, Kubernetes and orchestration tools. Proficiency in creating data visualizations specifically for graphs and networks using tools like Matplotlib, Seaborn, or Plotly. Understanding of data manipulation and analysis using libraries such as Pandas and Numpy Problem-solving, analytical expertise, and troubleshooting abilities with attention to details.

We are seeking a highly skilled AI/ML Engineer to join our team. As an AI/ML Engineer, you will be responsible for designing, implementing, and optimizing machine learning solutions, encompassing traditional models, deep learning architectures, and generative AI systems. Your role will involve collaborating with data engineers and cross-functional teams to create scalable, ethical, and high-performance AI/ML solutions that contribute to business growth. Your key responsibilities will include developing, implementing, and optimizing AI/ML models using both traditional machine learning and deep learning techniques. You will also design and deploy generative AI models for innovative business applications, in addition to working closely with data engineers to establish and maintain high-quality data pipelines and preprocessing workflows. Integrating responsible AI practices to ensure ethical, explainable, and unbiased model behavior will be a crucial aspect of your role. Furthermore, you will be expected to develop and maintain MLOps workflows to streamline training, deployment, monitoring, and continuous integration of ML models. Your expertise will be essential in optimizing large language models (LLMs) for efficient inference, memory usage, and performance. Collaboration with product managers, data scientists, and engineering teams to seamlessly integrate AI/ML into core business processes will also be part of your responsibilities. Rigorous testing, validation, and benchmarking of models to ensure accuracy, reliability, and robustness are essential aspects of this role. To be successful in this position, you must possess a strong foundation in machine learning, deep learning, and statistical modeling techniques. Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML frameworks is required. Proficiency in Python and ML engineering tools such as MLflow, Kubeflow, or SageMaker is also necessary. Experience in deploying generative AI solutions, understanding responsible AI concepts, solid experience with MLOps pipelines, and proficiency in optimizing transformer models or LLMs for production workloads are key qualifications for this role. Additionally, familiarity with cloud services (AWS, GCP, Azure), containerized deployments (Docker, Kubernetes), as well as excellent problem-solving and communication skills are essential. Ability to work collaboratively with cross-functional teams is also a crucial requirement. Preferred qualifications include experience with data versioning tools like DVC or LakeFS, exposure to vector databases and retrieval-augmented generation (RAG) pipelines, knowledge of prompt engineering, fine-tuning, and quantization techniques for LLMs, familiarity with Agile workflows and sprint-based delivery, and contributions to open-source AI/ML projects or published papers in conferences/journals. Join our team at Lucent Innovation, an India-based IT solutions provider, and enjoy a work environment that promotes work-life balance. With a focus on employee well-being, we offer 5-day workweeks, flexible working hours, and a range of indoor/outdoor activities, employee trips, and celebratory events throughout the year. At Lucent Innovation, we value our employees" growth and success, providing in-house training, as well as quarterly and yearly rewards and appreciation. Perks: - 5-day workweeks - Flexible working hours - No hidden policies - Friendly working environment - In-house training - Quarterly and yearly rewards & appreciation,

The Data Science and AI/ML team collaborates across the organization to identify, develop and deliver Artificial Intelligence (AI) and Machine Learning (ML) powered software solutions that improve patient outcomes, delight partners and customers, and enhance business operations in an AI First fashion. The team works on projects ranging from building sophisticated models to delivering personalized recommendations to patients, analyzing sleep data to optimize equipment and settings, proactively identifying health risk factors, and optimizing global supply chain operations. As a Sr. Machine Learning Engineer, you will play a crucial role in contributing to and leading the development of ML architecture and operations. Your responsibilities include optimizing time to market and quality of AI/ML applications by working on projects within the AI operations team. You will ensure that global AI/ML systems are production-grade, scalable, and utilize cutting-edge technology and methodology. Additionally, you will collaborate with stakeholders, mentor junior team members, and engage with business stakeholders. Key Responsibilities: - Collaborate with Product Management, Engineering, and other stakeholders to create impactful AI/ML features and products. - Work closely with Data Scientists and Data Engineers to own the end-to-end process, train junior team members, and maintain AI/ML architectures. - Document model design, experiments, tests, and outcomes, and stay informed of industry trends. - Implement process improvements, build production-level AI/ML systems, and support technical issues for stakeholders. - Participate in Code Review and handle escalated incidents to resolution. Requirements: - 4+ years of industry experience in Machine Learning Engineering, Data Science, or Data Engineering. - M.S or PhD in Data Science/Machine Learning or related areas. - Proficiency in data analytics systems development, model building, and online deployment. - Experience in building scalable AI/ML systems for various advanced problems. - Hands-on experience with large datasets, Python, and cloud-based tools. - Strong mathematical foundation and knowledge of machine learning techniques. Joining the team means more than just a job it's an opportunity to be part of a culture that values excellence, diversity, and innovation. If you are looking for a challenging and supportive environment where your ideas are encouraged, apply now to be a part of our team dedicated to making the world a healthier place. We are committed to reviewing every application we receive.,

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Lead Consultant - ML/CV Ops Engineer ! We are seeking a highly skilled ML CV Ops Engineer to join our AI Engineering team. This role is focused on operationalizing Computer Vision models&mdashensuring they are efficiently trained, deployed, monitored , and retrained across scalable infrastructure or edge environments. The ideal candidate has deep technical knowledge of ML infrastructure, DevOps practices, and hands-on experience with CV pipelines in production. You&rsquoll work closely with data scientists, DevOps, and software engineers to ensure computer vision models are robust, secure, and production-ready always. Key Responsibilities: End-to-End Pipeline Automation: Build and maintain ML pipelines for computer vision tasks (data ingestion, preprocessing, model training, evaluation, inference). Use tools like MLflow , Kubeflow, DVC, and Airflow to automate workflows. Model Deployment & Serving: Package and deploy CV models using Docker and orchestration platforms like Kubernetes. Use model-serving frameworks (TensorFlow Serving, TorchServe , Triton Inference Server) to enable real-time and batch inference. Monitoring & Observability: Set up model monitoring to detect drift, latency spikes, and performance degradation. Integrate custom metrics and dashboards using Prometheus, Grafana, and similar tools. Model Optimization: Convert and optimize models using ONNX, TensorRT , or OpenVINO for performance and edge deployment. Implement quantization, pruning, and benchmarking pipelines. Edge AI Enablement (Optional but Valuable): Deploy models on edge devices (e.g., NVIDIA Jetson, Coral, Raspberry Pi) and manage updates and logs remotely. Collaboration & Support: Partner with Data Scientists to productionize experiments and guide model selection based on deployment constraints. Work with DevOps to integrate ML models into CI/CD pipelines and cloud-native architecture. Qualifications we seek in you! Minimum Qualifications Bachelor&rsquos or Master&rsquos in Computer Science , Engineering, or a related field. Sound experience in ML engineering, with significant work in computer vision and model operations. Strong coding skills in Python and familiarity with scripting for automation. Hands-on experience with PyTorch , TensorFlow, OpenCV, and model lifecycle tools like MLflow , DVC, or SageMaker. Solid understanding of containerization and orchestration (Docker, Kubernetes). Experience with cloud services (AWS/GCP/Azure) for model deployment and storage. Preferred Qualifications: Experience with real-time video analytics or image-based inference systems. Knowledge of MLOps best practices (model registries, lineage, versioning). Familiarity with edge AI deployment and acceleration toolkits (e.g., TensorRT , DeepStream ). Exposure to CI/CD pipelines and modern DevOps tooling (Jenkins, GitLab CI, ArgoCD ). Contributions to open-source ML/CV tooling or experience with labeling workflows (CVAT, Label Studio). Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Senior Principal Consultant - Data Scientists with Computer vision experience! We are seeking a tenured and highly skilled Data Scientist with deep expertise in Computer Vision and a strong foundation in AI/ML modeling. The ideal candidate will not only lead the development of intelligent vision systems but will also serve as a technical mentor, providing guidance to junior data scientists on model selection, optimization, and deployment strategies. Experience in domains such as energy, power generation, industrial equipment, or manufacturing will be considered a strong advantage, as the role involves solving real-world visual AI/ML problems in industrial environments. Key Responsibilities: Lead CV Projects: Design and deliver Computer Vision models across a range of use cases (e.g., anomaly detection, visual inspections, OCR, predictive maintenance). Model Development: Develop, evaluate, and optimize state-of-the-art AI/ML models (e.g., CNNs, Vision Transformers, YOLO, Faster R-CNN, etc.). Mentorship: Guide junior and mid-level data scientists on best practices in feature engineering, model selection, evaluation metrics, and problem-solving strategies. Domain Translation: Translate complex industrial problems into AI-driven CV solutions that can scale in production environments. Collaboration: Work closely with software engineers, MLOps , and business teams to ensure model integration and operational success. Code Quality & Experimentation: Drive code modularity, reproducibility, and experimentation through use of ML pipelines, version control, and testing. Innovation & Research: Stay current with latest CV and AI/ML advancements and apply them appropriately to business problems. Stakeholder Communication: Present insights, models, and outcomes in a clear and impactful way to both technical and non-technical stakeholders. Qualifications we seek in you! Minimum Qualifications Master&rsquos or PhD in Computer Science, Machine Learning, AI, Electrical Engineering, or a related field. industry experience in building and deploying machine learning models, with a strong portfolio in Computer Vision. Deep expertise in ML frameworks and CV libraries such as PyTorch , TensorFlow, OpenCV, Detectron2, MMDetection , etc. Solid understanding of core AI/ML algorithms - classification, regression, segmentation, object detection, time-series, clustering, etc. Experience with MLOps tools (e.g., MLflow , DVC, Kubeflow) and cloud platforms (AWS/GCP/Azure). Strong communication , leadership, and team collaboration skills . Preferred Qualifications: Prior experience in domains such as energy, utilities, power generation, or industrial systems is highly preferred. Experience deploying CV models within real-time environments. Contributions to open-source CV projects or published research. Proficient in statistical modelling, machine learning techniques, AI algorithms, and generative model development using large language models such as GPT-3, BERT, or similar frameworks like RAG, Knowledge Graphs etc. Lead the development of CI/CD pipelines and standardize deployment frameworks. Strong Python programming skills. Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

As a Senior Machine Learning Engineer in Healthcare Revenue Cycle Management (RCM) at BigRio, you will have the opportunity to work on cutting-edge projects with a team of top-tier professionals. BigRio is a remote-based technology consulting firm that specializes in delivering advanced software solutions, including custom development, cloud data platforms, AI/ML integrations, and data analytics. You are a problem-solver who enjoys owning high-impact initiatives and is excited about innovation in machine learning. You stay current with evolving technologies and bring hands-on experience in building, evaluating, and deploying models. Your ability to communicate comfortably across technical and non-technical audiences is crucial. Most importantly, you are motivated by the opportunity to improve healthcare systems through smart, data-driven solutions. The RCM product at BigRio is central to the platform, supporting billions in revenue processing each year. As part of the Data Science team, you will work on challenging problems in automation and optimization using advanced ML techniques. Collaboration with engineering is key to deliver intelligent features that seamlessly integrate into the healthcare platform. Additionally, you will be involved in managing production-grade ML deployments using modern cloud tools. Key Responsibilities: - Identify opportunities to apply machine learning to RCM workflows. - Design, develop, and deploy ML-based production services. - Adhere to and advocate for best practices in model development and engineering. - Conduct rigorous testing and validation of models and code. - Contribute to the development of internal tools and team standards. Qualifications: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. - 3-5 years of hands-on experience building, evaluating, and deploying ML models. - Strong programming skills in Python, with proficiency in SQL and Unix environments. - Experience with deep learning and neural network architectures (preferred). - Familiarity with training and fine-tuning LLMs or generative AI models (bonus). - Exposure to NLP or computer vision is a plus. - Experience with AWS and tools such as Kubernetes, Kubeflow, or EKS is a bonus. - Excellent communication skills, both written and verbal. BigRio is an Equal Opportunity Employer committed to diversity and inclusion in the workplace.,

We are hiring for the position of Machine Learning Engineer and the role is based out Bangalore Shift Timings - General Shift Interested Candidates can send CV directly at Pratibha@myndsol.com Responsibilities: 1. Machine Learning Development & Deployment Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross-sell/upsell opportunity identification. Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience. Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow. 2. Cross-Functional ML Use Cases Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases. Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3. Model Governance and MLOps Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments. Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure). 4. Data Engineering and Feature Architecture Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks. Perform feature selection, transformation, and engineering aligned with each domains business logic. 5. Communication & Stakeholder Collaboration Present technical insights and model results to business and executive stakeholders in a clear, actionable format. Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects. Qualifications: Required: 5+ years of experience in machine learning, data science, or AI engineering, with a strong software engineering foundation. Proficiency in Python, and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar. Experience deploying models into production using ML pipelines and orchestration frameworks. Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI). Preferred: Experience supporting business functions such as Finance, Sales, or Operations with ML use cases. Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store). Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce). Background in statistics, forecasting, optimization, or recommendation systems

About Apexon: Apexon is a digital-first technology services firm specializing in accelerating business transformation and delivering human-centric digital experiences. We have been meeting customers wherever they are in the digital lifecycle and helping them outperform their competition through speed and innovation.Apexon brings together distinct core competencies in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences – to help businesses capitalize on the unlimited opportunities digital offers. Our reputation is built on a comprehensive suite of engineering services, a dedication to solving clients’ toughest technology problems, and a commitment to continuous improvement. Backed by Goldman Sachs Asset Management and Everstone Capital, Apexon now has a global presence of 15 offices (and 10 delivery centers) across four continents. We enable #HumanFirstDigital Key Responsibilities: Design, develop, and maintain CI/CD pipelines for ML models and data workflows. Collaborate with data science teams to productionize models using tools like MLflow, Kubeflow, or SageMaker. Automate training, validation, testing, and deployment of machine learning models. Monitor model performance, drift, and retraining needs. Ensure version control of datasets, code, and model artifacts. Implement model governance, audit trails, and reproducibility. Optimize model serving infrastructure (REST APIs, batch/streaming inference). Integrate ML solutions with cloud services (AWS, Azure, GCP). Ensure security, compliance, and reliability of ML systems. Required Skills and Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. 4+ years of experience in MLOps, DevOps, or ML engineering roles. Strong experience with ML pipeline tools (MLflow, Kubeflow, TFX, SageMaker Pipelines). Proficiency in containerization and orchestration tools (Docker, Kubernetes, Airflow). Strong Python coding skills and familiarity with ML libraries (scikit-learn, TensorFlow, PyTorch). Experience with cloud platforms (AWS, Azure, GCP) and their ML services. Knowledge of CI/CD tools (GitLab CI/CD, Jenkins, GitHub Actions). Familiarity with monitoring/logging tools (Prometheus, Grafana, ELK, Sentry). Understanding of data versioning (DVC, LakeFS) and feature stores (Feast, Tecton). Strong grasp of model testing, validation, and monitoring in production environments. Our Commitment to Diversity & Inclusion: Did you know that Apexon has been Certified™ by Great Place To Work®, the global authority on workplace culture, in each of the three regions in which it operates: USA (for the fourth time in 2023), India (seven consecutive certifications as of 2023), and the UK.Apexon is committed to being an equal opportunity employer and promoting diversity in the workplace. We take affirmative action to ensure equal employment opportunity for all qualified individuals. Apexon strictly prohibits discrimination and harassment of any kind and provides equal employment opportunities to employees and applicants without regard to gender, race, color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law. You can read about our Job Applicant Privacy policy here Job Applicant Privacy Policy (apexon.com) Our Perks and Benefits: Our benefits and rewards program has been thoughtfully designed to recognize your skills and contributions, elevate your learning/upskilling experience and provide care and support for you and your loved ones. As an Apexon Associate, you get continuous skill-based development, opportunities for career advancement, and access to comprehensive health and well-being benefits and assistance. We also offer: Group Health Insurance covering family of 4 Term Insurance and Accident Insurance Paid Holidays & Earned Leaves Paid Parental LeaveoLearning & Career Development Employee Wellness

The ideal candidate for this position in Ahmedabad should be a graduate with at least 3 years of experience. At Bytes Technolab, we strive to create a cutting-edge workplace infrastructure that empowers our employees and clients. Our focus on utilizing the latest technologies enables our development team to deliver high-quality software solutions for a variety of businesses. You will be responsible for leveraging your 3+ years of experience in Machine Learning and Artificial Intelligence to contribute to our projects. Proficiency in Python programming and relevant libraries such as NumPy, Pandas, and scikit-learn is essential. Hands-on experience with frameworks like PyTorch, TensorFlow, Keras, Facenet, and OpenCV will be key in your role. Your role will involve working with GPU acceleration for deep learning model development using CUDA, cuDNN. A strong understanding of neural networks, computer vision, and other AI technologies will be crucial. Experience with Large Language Models (LLMs) like GPT, BERT, LLaMA, and familiarity with frameworks such as LangChain, AutoGPT, and BabyAGI are preferred. You should be able to translate business requirements into ML/AI solutions and deploy models on cloud platforms like AWS SageMaker, Azure ML, and Google AI Platform. Proficiency in ETL pipelines, data preprocessing, and feature engineering is required, along with experience in MLOps tools like MLflow, Kubeflow, or TensorFlow Extended (TFX). Expertise in optimizing ML/AI models for performance and scalability across different hardware architectures is necessary. Knowledge of Natural Language Processing (NLP), Reinforcement Learning, and data versioning tools like DVC or Delta Lake is a plus. Skills in containerization tools like Docker and orchestration tools like Kubernetes will be beneficial for scalable deployments. You should have experience in model evaluation, A/B testing, and establishing continuous training pipelines. Working in Agile/Scrum environments with cross-functional teams, understanding ethical AI principles, model fairness, and bias mitigation techniques are important. Familiarity with CI/CD pipelines for machine learning workflows and the ability to communicate complex concepts to technical and non-technical stakeholders will be valuable.,

You will be responsible for designing, building, and deploying scalable NLP/ML models for real-world applications. Your role will involve fine-tuning and optimizing Large Language Models (LLMs) using techniques like LoRA, PEFT, or QLoRA. You will work with transformer-based architectures such as BERT, GPT, LLaMA, and T5, and develop GenAI applications using frameworks like LangChain, Hugging Face, OpenAI API, or RAG (Retrieval-Augmented Generation). Writing clean, efficient, and testable Python code will be a crucial part of your tasks. Collaboration with data scientists, software engineers, and stakeholders to define AI-driven solutions will also be an essential aspect of your work. Additionally, you will evaluate model performance and iterate rapidly based on user feedback and metrics. The ideal candidate should have a minimum of 3 years of experience in Python programming with a strong understanding of ML pipelines. A solid background and experience in NLP, including text preprocessing, embeddings, NER, and sentiment analysis, are required. Proficiency in ML libraries such as scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, and spaCy is essential. Experience with GenAI concepts, including prompt engineering, LLM fine-tuning, and vector databases like FAISS and ChromaDB, will be beneficial. Strong problem-solving and communication skills are highly valued, along with the ability to learn new tools and work both independently and collaboratively in a fast-paced environment. Attention to detail and accuracy is crucial for this role. Preferred skills include theoretical knowledge or experience in Data Engineering, Data Science, AI, ML, RPA, or related domains. Certification in Business Analysis or Project Management from a recognized institution is a plus. Experience in working with agile methodologies such as Scrum or Kanban is desirable. Additional experience in deep learning and transformer architectures and models, prompt engineering, training LLMs, and GenAI pipeline preparation will be advantageous. Practical experience in integrating LLM models like ChatGPT, Gemini, Claude, etc., with context-aware capabilities using RAG or fine-tuning models is a plus. Knowledge of model evaluation and alignment, as well as metrics to calculate model accuracy, is beneficial. Data curation from sources for RAG preprocessing and development of LLM pipelines is an added advantage. Proficiency in scalable deployment and logging tooling, including skills like Flask, Django, FastAPI, APIs, Docker containerization, and Kubeflow, is preferred. Familiarity with Lang Chain, LlamaIndex, vLLM, HuggingFace Transformers, LoRA, and a basic understanding of cost-to-performance tradeoffs will be beneficial for this role.,

At PwC, our data and analytics team focuses on utilizing data to drive insights and support informed business decisions. We leverage advanced analytics techniques to assist clients in optimizing their operations and achieving strategic goals. As a data analysis professional at PwC, your role will involve utilizing advanced analytical methods to extract insights from large datasets, enabling data-driven decision-making. Your expertise in data manipulation, visualization, and statistical modeling will be pivotal in helping clients solve complex business challenges. PwC US - Acceleration Center is currently seeking a highly skilled MLOps/LLMOps Engineer to play a critical role in deploying, scaling, and maintaining Generative AI models. This position requires close collaboration with data scientists, ML/GenAI engineers, and DevOps teams to ensure the seamless integration and operation of GenAI models within production environments at PwC and for our clients. The ideal candidate will possess a strong background in MLOps practices and a keen interest in Generative AI technologies. With a preference for candidates with 4+ years of hands-on experience, core qualifications for this role include: - 3+ years of experience developing and deploying AI models in production environments, alongside 1 year of working on proofs of concept and prototypes. - Proficiency in software development, including building and maintaining scalable, distributed systems. - Strong programming skills in languages such as Python and familiarity with ML frameworks like TensorFlow and PyTorch. - Knowledge of containerization and orchestration tools like Docker and Kubernetes. - Understanding of cloud platforms such as AWS, GCP, and Azure, including their ML/AI service offerings. - Experience with continuous integration and delivery tools like Jenkins, GitLab CI/CD, or CircleCI. - Familiarity with infrastructure as code tools like Terraform or CloudFormation. Key Responsibilities: - Develop and implement MLOps strategies tailored for Generative AI models to ensure robustness, scalability, and reliability. - Design and manage CI/CD pipelines specialized for ML workflows, including deploying generative models like GANs, VAEs, and Transformers. - Monitor and optimize AI model performance in production, utilizing tools for continuous validation, retraining, and A/B testing. - Collaborate with data scientists and ML researchers to translate model requirements into scalable operational frameworks. - Implement best practices for version control, containerization, and orchestration using industry-standard tools. - Ensure compliance with data privacy regulations and company policies during model deployment. - Troubleshoot and resolve issues related to ML model serving, data anomalies, and infrastructure performance. - Stay updated with the latest MLOps and Generative AI developments to enhance AI capabilities. Project Delivery: - Design and implement scalable deployment pipelines for ML/GenAI models to transition them from development to production environments. - Oversee the setup of cloud infrastructure and automated data ingestion pipelines to meet GenAI workload requirements. - Create detailed documentation for deployment pipelines, monitoring setups, and operational procedures. Client Engagement: - Collaborate with clients to understand their business needs and design ML/LLMOps solutions. - Present technical approaches and results to technical and non-technical stakeholders. - Conduct training sessions and workshops for client teams. - Create comprehensive documentation and user guides for clients. Innovation And Knowledge Sharing: - Stay updated with the latest trends in MLOps/LLMOps and Generative AI. - Develop internal tools and frameworks to accelerate model development and deployment. - Mentor junior team members and contribute to technical publications. Professional And Educational Background: - Any graduate / BE / B.Tech / MCA / M.Sc / M.E / M.Tech / Masters Degree / MBA,

As a Python Engineer with 2-4 years of experience, you will be responsible for building, deploying, and scaling Python applications along with AI/ML solutions. Your strong programming skills will be put to use in developing intelligent solutions and collaborating closely with clients and software engineers to implement machine learning models. You should be an expert in Python, with advanced knowledge of Flask/FastAPI and server programming to implement complex business logic. Understanding fundamental design principles behind scalable applications is crucial. Independently designing, developing, and deploying machine learning models and AI algorithms tailored to business requirements will be a key aspect of your role. Your responsibilities will include solving complex technical challenges through innovative AI/ML solutions, building and maintaining integrations (e.g., APIs) for machine learning models, conducting data preprocessing and feature engineering, and optimizing datasets for model training and inference. Monitoring and continuously improving model performance in production environments, focusing on scalability and efficiency, will also be part of your tasks. Managing model deployment, monitoring, and scaling using tools like Docker, Kubernetes, and cloud services will be essential. You will need to develop integration strategies for smooth communication between APIs and troubleshoot integration issues. Creating and maintaining comprehensive documentation for AI/ML projects will be necessary, along with staying updated on emerging trends and technologies in AI/ML. Key Skills Required: - Proficiency in Python, R, or similar languages commonly used in ML/AI development - Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML libraries - Strong knowledge of data preprocessing, data cleaning, and feature engineering - Familiarity with model deployment using Docker, Kubernetes, or cloud platforms - Understanding of statistical methods, probability, and data-driven decision-making processes - Proficient in querying databases for ML projects - Experience with ML lifecycle management tools like MLflow, Kubeflow - Familiarity with NLP frameworks for language-based AI solutions - Exposure to computer vision techniques - Experience with managed ML services like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform - Familiarity with agile workflows and DevOps or CI/CD pipelines Good to Have Skills: - Exposure to big data processing tools like Spark, Hadoop - Experience with agile development methodologies The job location for this role is in Ahmedabad/Pune, and the required educational qualifications include a UG degree in BE/BTech or PG degree in ME/M-Tech/MCA/MSC-IT/Data Science, AI, Machine Learning, or a related field.,

As a highly skilled Senior Machine Learning Engineer, you will leverage your expertise in Deep Learning, Large Language Models (LLMs), and MLOps/LLMOps to design, optimize, and deploy cutting-edge AI solutions. Your responsibilities will include developing and scaling deep learning models, fine-tuning LLMs (e.g., GPT, Llama), and implementing robust deployment pipelines for production environments. You will be responsible for designing, training, fine-tuning, and optimizing deep learning models (CNNs, RNNs, Transformers) for various applications such as NLP, computer vision, or multimodal tasks. Additionally, you will fine-tune and adapt LLMs for domain-specific tasks like text generation, summarization, and semantic similarity. Experimenting with RLHF (Reinforcement Learning from Human Feedback) and alignment techniques will also be part of your role. In the realm of Deployment & Scalability (MLOps/LLMOps), you will build and maintain end-to-end ML pipelines for training, evaluation, and deployment. Deploying LLMs and deep learning models in production environments using frameworks like FastAPI, vLLM, or TensorRT is crucial. You will optimize models for low-latency, high-throughput inference and implement CI/CD workflows for ML systems using tools like MLflow and Kubeflow. Monitoring & Optimization will involve setting up logging, monitoring, and alerting for model performance metrics such as drift, latency, and accuracy. Collaborating with DevOps teams to ensure scalability, security, and cost-efficiency of deployed models will also be part of your responsibilities. The ideal candidate will possess 5-7 years of hands-on experience in Deep Learning, NLP, and LLMs. Strong proficiency in Python, PyTorch, TensorFlow, Hugging Face Transformers, and LLM frameworks is essential. Experience with model deployment tools like Docker, Kubernetes, and FastAPI, along with knowledge of MLOps/LLMOps best practices and familiarity with cloud platforms (AWS, GCP, Azure) are required qualifications. Preferred qualifications include contributions to open-source LLM projects, showcasing your commitment to advancing the field of machine learning.,

You are an experienced Lead Data Scientist with a minimum of 8 years of professional experience, including at least 6 years in a data science role. Your expertise lies in statistical modeling, machine learning, deep learning, and GenAI. Proficiency in Python is a must, along with hands-on experience in optimizing code for performance. You excel in data preprocessing, feature engineering, data visualization, hyperparameter tuning, and have a solid understanding of database concepts, especially while working with large datasets. You have experience deploying and scaling machine learning models in a production environment, along with familiarity with machine learning operations (MLOps) and related tools. Your knowledge extends to Generative AI concepts and LLM finetuning, supported by excellent communication and collaboration skills. Your responsibilities as a Lead Data Scientist include guiding and mentoring a high-performance team on the latest technology landscape, patterns, and design standards. You provide strategic direction and technical leadership for AI initiatives, leading the design and architecture of complex AI systems. Your role involves developing and deploying machine learning/deep learning models to address key business challenges, applying various techniques in statistical modeling, data preprocessing, feature engineering, and more. You are proficient in areas such as computer vision, predictive analytics, natural language processing, time series analysis, and recommendation systems. Furthermore, you design and optimize data pipelines for model training and deployment, utilizing model serving frameworks and APIs for integration with other systems. Your qualifications include a Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Your primary skills encompass Python, Data Science concepts, Pandas, NumPy, Matplotlib, Artificial Intelligence, Statistical Modeling, Machine Learning, Natural Language Processing (NLP), Deep Learning, Model Serving Frameworks, MLOps, Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems, Generative AI, and proficiency in Cloud Computing Platforms. Your secondary skills involve expertise in designing scalable and efficient model architectures, the ability to assess and forecast financial requirements of data science projects, and strong communication skills for conveying technical concepts to stakeholders. As a Lead Data Scientist, you stay updated with the latest advancements in data science and machine learning, particularly in generative AI, to evaluate their potential applications and serve as a primary point of contact for client managers.,