Develop and prototype machine learning models in collaboration with data scientists,ensuring scalability and production readiness. Implement model experimentation frameworks and methodologies to optimize model performance and reliability. Package, serialize, and optimize machine learning models for efficient deployment in production environments. Manage model registries and version control, ensuring traceability and reproducibility of deployed models. Implement model deployment pipelines and integrate models with CI/CD processes for automated testing and deployment.

15 years of IT experience for Solution Architect currently hands on with minimum of 5 years of experience on Azure having technically guided managed and governed the team Flexible to work in the shifts Primary skills Strong communication skills and experience in managing various stakeholder relationships to gain consensus on complex technical solutions. Experience in architecting designing and implementing solutions on premises in the cloud and using hybrid models. Hands on experience in deploying a variety of generative models. In-depth experience in finetuning and customizing pretrained AI models with good understanding of various patterns and practices in AI data engineering and large data processing. Hands on in Prompt Engineering Azure Open AI Form Recognizer Cognitive Search Vector Databases. Develop and deliver upskilling sessions to the customer. Python and PySpark Secondary Skills MLOps and LLMOps Certifications Must Have Microsoft Certified Azure Developer Associate AZ204 Certifications Good to Have Microsoft Certified Solutions Architect Expert AZ303 AZ304 AI102 Microsoft Certified Azure AI Engineer Associate DP100 Microsoft Certified Azure Data Scientist Associate Databricks Professional Certificate in Large Language Models Microsoft Certified Azure Solutions Architect Expert Soft Skills Good customer connects Prepare solution presentations Positive attitude and excellent communication skills to manage customer calls Excellent problem solving skills Good communication Educational Qualification Minimum BCA MCA BE BTech or equivalent Preferred MTech MS

Position Overview As the Machine Learning Enablement Engineering Manager within Fords Data Platforms and Engineering (DP&E) organization, you are a key leader responsible for guiding and developing a team of engineers focused on delivering high-impact, scalable machine learning solutions to address critical business challenges within DP&E. Your primary focus will be on building and maintaining the platform infrastructure and processes that empower data scientists and ML engineers to rapidly deploy and scale their solutions into production. You will work closely with Product Managers, Architects, Data Scientists, and other key stakeholders to drive engineering excellence, promote innovation, and uphold best practices. This role is less about building individual ML models and more about creating robust, reliable, and scalable solutions that allow others to deliver value effectively. Your leadership is crucial in driving the success of our machine learning initiatives. Your ability to guide and develop a team of engineers, while maintaining alignment with Fords strategic goals, will be key to delivering world-class, production-ready ML solutions that power Ford’s transformation into a data-driven enterprise. You should be a highly hands-on engineering leader with a proven track record of delivering complex, scalable solutions. While a deep understanding of ML concepts is beneficial, your primary focus will be on platform engineering, DevOps, and building robust, maintainable infrastructure. You will define processes for technical platforms, conceive application prototypes, and mentor your team in best practices. Your day-to-day responsibilities will involve designing and managing the organization's ML infrastructure architecture, ensuring data is efficiently processed, stored, and accessed to support ML model development and deployment. You will be pivotal in delivering these solutions on time and within budget. Responsibilities: I. Engineering Leadership & Management: Proven experience (7+ years) in a leadership role managing engineering teams , ideally with a focus on platform engineering, MLOps, or similar areas. Experience managing remote teams is a plus. Experience leading and mentoring engineering teams , fostering a culture of innovation, continuous learning, and technical excellence. Demonstrated ability to drive strategic technical decisions and ensure alignment with broader organizational goals. Proven ability to build and maintain high-performing teams , promoting accountability, ownership, and collaboration. Experience with performance management, including conducting performance reviews and providing constructive feedback. Excellent communication and interpersonal skills , with a proven ability to cultivate cross-functional collaboration and build strong relationships with stakeholders at all levels. II. Agile & Scrum Practices: Deep understanding and practical experience with Agile methodologies (Scrum, Kanban) , including facilitating daily stand-ups, sprint planning, backlog grooming, and sprint retrospectives. Experience working closely with Product Managers to align engineering efforts with product goals, ensure well-defined user stories, and manage priorities effectively. Proven ability to ensure engineering rigor in story hygiene , including clear acceptance criteria, well-defined dependencies, and a focus on deliverability within the sprint. III. Technical Expertise & Accountability: Deep understanding of platform engineering principles and experience designing, building, and maintaining scalable and reliable infrastructure for ML workloads. Expertise in DevOps practices , including CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), infrastructure-as-code (Terraform, Ansible, CloudFormation), and automation. Proficiency in at least one programming language (e.g., Python, Java) sufficient to effectively communicate with and guide your engineering team. You won’t be expected to contribute to team capacity by coding, but you need to be able to speak the language of your engineers. Strong understanding of cloud solutions and offerings (preferably GCP services – Compute Engine, Kubernetes Engine, Cloud Functions, BigQuery, Pub/Sub, Cloud Storage, Vertex AI). Experience with other major cloud providers (AWS, Azure) is also valuable. Experience with designing and implementing microservices and serverless architectures. Experience with containerization (Docker, Kubernetes) is highly beneficial. Experience with monitoring and optimizing platform performance , ensuring systems are running efficiently and meeting SLAs. Proven ability to lead incident management efforts and implement continuous improvements to enhance reliability. Commitment to best engineering practices , including code reviews, testing, and documentation. A focus on building maintainable and scalable systems is essential. IV. Operational Excellence & Cost Optimization: Proven ability to drive cost optimization initiatives , particularly in cloud infrastructure and resource usage, aligning with Ford’s broader cost-reduction goals. Experience tracking and reporting key metrics for your domain/platform related to team performance, including quality and operational efficiency.

Role Summary The ML Ops Engineer will be responsible for designing building and maintaining the infrastructure and processes for deploying and managing machine learning models in production Responsibilities Understand and translate business and functional needs into machine learning problem statements Translate complex machine learning problem statements into specific deliverables and requirements Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements Translate machine learning algorithms into productionlevel code Collaborate with development teams to test and deploy machine learning models Monitor the performance of deployed models track data or concept drift and update or retrain models as needed Ensure adherence to performance standards and compliance with data security requirements Keep abreast with new tools algorithms and techniques in machine learning and work to implement them in the organization Proficient in Python scripting and familiarity with Python packaging (e.g. PyPI, pip, virtualenv) Hands on experience with ML workflow tools like MLflow, Kubeflow, MLRun, DVC, Airflow leveraging Python integrations. Automate model training, testing and deployment processes using CI/CD tools. Experience with cloud platform preferably GCP and their Python SDKs. Experience with SQL and Python-based ETL processes. Familiarity with data processing frameworks like Apache, Spark or Dask using PySpark or similar Python interfaces. Collaborate with development teams to test, optimize ML workflows and deploy/integrate machine learning models into applications. Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements Translate machine learning algorithms into production-level code Knowledge of version control systems (e.g. git) and collaborative coding practices. Monitor the performance of deployed models, track data or concept drift, and update or retrain models as needed Ensure adherence to performance standards and compliance with data security requirements Keep abreast with new tools, algorithms and techniques in machine learning and work to implement them in the organization Education A bachelors degree in computer science data science applied mathematics software engineering or related masters degree preferred Specialization in applied machine learning or machine learning infrastructure preferred Experience 5-7 years of experience in developing and deploying enterprise scale machine learning solutions in a software engineering adjacent field Experience developing and debugging in Python Exposure to architectural patterns of largescale software applications Experience with REST API development in Python (e.g. Flask, Fast API) for model serving. Experience in developing, trouble shooting and resolving issues related to ML systems in production. Understanding of DevOps principles and exposure to architectural patterns of large-scale software applications Required skills Knowledge of working on any Cloud Environment GCP preferred Proficiency in deploying machine learning algorithms as production ready API services Advanced programming skills with Python Ability to effectively communicate technical concepts and results to technical and business audiences in a comprehensive manner Ability to collaborate effectively across multiple teams and stakeholders including analytics teams development teams product management and operations Ability to work independently and in a fully remote environment Willingness and ability to stay up to date on new MLAI technologies and their potential impact on the company

Responsibilities: Customer & Architecture Interact with customers to define problem statements and technical requirements Design and architect AI/ML cloud solutions using AWS/GCP Create system architecture diagrams and establish best practices for ML systems Define data architecture and model serving strategies Technical Leadership & Development Guide team in building Python-based APIs and AI/ML models Design and implement ML pipeline automation Establish model deployment and monitoring architectures Review technical designs and approve implementation approaches Lead architectural decision-making and technical direction Project & Team Management Overall responsibility for project/program delivery Manage and mentor AI/ML engineers Lead sprint planning and technical grooming sessions Coordinate with cross-functional teams Balance resource allocation and technical debt Qualifications Must have at least 10 years of IT experience with 5+ years experience in designing, developing, deploying, and operationalizing AI/ML solutions. Experience in GenAI and Conversational AI LLMs, frameworks like Lang chain, Llama Index etc, Vector DBs like Pinecone, Elasticsearch, etc. Deep understanding and experience with popular Deep Learning and Machine Learning techniques and algorithms such as LLMs, Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM, Transformers, Time Series Forecasting, Segmentation, etc. Experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps/LLMOps, Optimization techniques, and AI solution Architecture. Experience with ML Pipelines both On-Prem and Cloud (like SageMaker) Expert in Python as well as open-source frameworks such as PyTorch, TensorFlow, NumPy, etc. Experience in designing cloud-native solutions using AI and other services from Azure, GCP, AWS, etc. Ability to lead Data Engineers and ML engineers to provide guidance on technical architecture and best practices.

Arcolab Pvt Ltd is looking for a Data Scientist, Pls find the JD below. 1.Modelling and developing Machine Learning solutions 2.Carrying out pre-processing, cleansing of structured and unstructured data for ML models 3.Implement solutions for prediction systems and machine learning challenges 4. Monitor and Maintain ML/AI builds post implementation 5. Follow Quality Assurance (QA) Compliance processes to ensure quality 6. Automations 7. Updating and maintaining Standards around Industry best practices Technical Skills Required: 1. Minimum 3 years of experience in a Data Scientist role 2. Experience in developing ML based solutions or functionality 3. Experience in Cloud technologies 4. Must be able to multitask and willing to work on multiple projects at a given time 5. Strong written and verbal communication skills 6. Ability to work to deadlines and manage expectations 7. Strong analytical and problem-solving skills

Role & responsibilities Hope akash meets the requirement. Design, develop, test, and deploy AI models and systems using various frameworks and tools. Monitor and evaluate the performance and accuracy of AI models and systems in production. Identify and troubleshoot issues related to data quality, model drift, scalability, and reliability. Optimize and automate the AI model lifecycle using MLOps best practices. Collaborate with data scientists, ML engineers, and business stakeholders to understand the requirements and objectives of AI projects. Research and implement new AI techniques and methodologies to improve the existing solutions and explore new opportunities. Research and Development: Stay abreast of the latest advancements in AI/GenIA, explore novel applications, and develop proof-of-concept projects. Model Selection and Fine-tuning: Select and fine-tune pretrained GenAI models (e.g., GPT4, LLaMa) for specific tasks, including text generation, image creation, code synthesis, etc. Data Exploration and Preparation: Prepare and analyze data sets for training and evaluation of GenAI models, ensuring data quality and addressing potential biases. Experimentation and Evaluation: Conduct experiments to assess the performance of GenAI models and compare different architectures and approaches. Application Integration: Develop and integrate GenAI models into existing or new applications, leveraging frameworks like LangChain for seamless orchestration. Collaborate with Cross-Functional Teams: Work closely with data scientists, software engineers, product managers, and designers to bring GenAI solutions to life. Contribute to Knowledge Sharing: Document your findings, share best practices, and contribute to the team's knowledge base. Preferred candidate profile Perks and benefits

Role & responsibilities : Top key skills for an experienced MLOps Engineer : Cloud & Infrastructure Expertise in AWS , GCP, or Azure, with experience in Kubernetes, Docker , and Terraform. CI/CD & Automation Strong knowledge of Jenkins , GitHub Actions, or GitLab CI/CD for ML model deployment . Model Monitoring & Optimization – Experience in tracking model performance, drift detection, and scaling ML pipelines . Data Engineering & Pipelines – Proficiency in Apache Airflow, Spark, or Kafka for ETL and feature engineering. Security & Compliance – Understanding of ML model security, access control, and regulatory compliance (GDPR, HIPAA). Preferred candidate profile : Qualification: B.Tech – CSE / M.Tech -Data Science or AI Perks and benefits : Best in the industry

Experience in building and deploying generative AI systems in real-world applications. Utilize advanced machine learning techniques to develop and train generative AI models. Collaborate with cross-functional teams to groom and preprocess large datasets for model training. Participating in due diligence activities to understand existing process, tech stack, platforms etc and expand/reuse the existing capabilities. Research and implement cutting-edge algorithms and architectures for generative AI applications. Optimize model performance and scalability for real-time inference and deployment. Stay current with industry trends and advancements in generative AI technology to drive innovation. Experiment with different hyperparameters and model configurations to improve generative AI model quality. Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation, Choose suitable DL algorithms, software, hardware and suggest integration methods. Ensure AI ML solutions are developed, and validations are performed in accordance with Responsible AI guidelines & Standards To closely monitor the Model Performance and ensure Model Improvements are done post Project Delivery Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services. Coach and mentor our team as we build scalable machine learning solutions Strong communication skills and an easy-going attitude Oversee development and implementation of assigned programs and guide teammates Carry out testing procedures to ensure systems are running smoothly Ensure that systems satisfy quality standards and procedures Build and manage strong relationships with stakeholders and various teams internally and externally, Provide direction and structure to assigned projects activities, establishing clear, precise goals, objectives and timeframes, run Project Governance calls with senior Stakeholders Strong problem-solving and critical thinking skills for complex AI problems. Excellent communication and teamwork abilities to collaborate with cross-functional teams. Proven track record of delivering innovative solutions using generative AI technologies. Ability to stay updated with the latest advancements in generative AI and adapt to new techniques and methodologies.

About the Role: We are seeking a highly experienced and visionary AI Senior Technical Lead to drive the technical direction and execution of our AI initiatives. This role is pivotal in shaping our AI strategy, leading a team of talented AI engineers, and ensuring the delivery of innovative and impactful AI solutions. As a technical leader, you will be responsible for defining architectural vision, driving best practices, and fostering a culture of technical excellence across diverse AI domains. Responsibilities: Technical Leadership & Strategy: Define and communicate the technical vision and strategy for AI projects. Lead the architectural design and implementation of complex AI systems. Evaluate and recommend emerging AI technologies and methodologies. Drive the adoption of best practices for AI development, deployment, and maintenance. Contribute to the strategic planning of AI initiatives, aligning them with business goals. Team Leadership & Mentorship: Lead and mentor a team of AI engineers, providing technical guidance and support. Foster a collaborative and innovative team environment. Conduct code reviews and ensure code quality. Identify and address skill gaps within the team. Facilitate knowledge sharing and continuous learning. AI System Development & Implementation: Oversee the development and deployment of scalable and robust AI solutions. Ensure the performance, reliability, and security of AI systems. Drive the development of AI pipelines and infrastructure. Lead the integration of AI models with existing systems and applications. Troubleshoot and resolve complex technical issues. Research & Innovation: Stay abreast of the latest advancements in AI and machine learning. Identify opportunities to apply AI to solve complex business challenges, including opportunities within Generative AI. Drive research and development efforts to explore new AI technologies. Evaluate and prototype new AI solutions. Collaboration & Communication: Collaborate with cross-functional teams, including product managers, data scientists, and software engineers. Communicate complex technical concepts to both technical and non-technical audiences. Provide technical presentations and demonstrations. Document technical designs, code, and processes. Required Skills and Qualifications: Education: Batchelors or Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: 8+ years of experience in AI/ML development, with a proven track record of leading complex AI projects. Extensive experience in designing and implementing scalable AI systems. Proven experience in leading and mentoring technical teams. Deep understanding of machine learning and deep learning algorithms and architectures. Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services. Technical Skills: Expertise in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Strong understanding of AI infrastructure and deployment strategies. Experience with MLOps best practices. Experience with vector databases, and LLM implementations. Proficiency in software development principles and best practices. Experience with containerization and orchestration tools (e.g., Docker, Kubernetes). Soft Skills: Exceptional leadership and communication skills. Strong problem-solving and analytical skills. Ability to think strategically and drive innovation. Excellent interpersonal and collaboration skills. Strong presentation skills. Preferred Qualifications: Experience with Generative AI technologies and methodologies (e.g., GANs, Diffusion Models, Transformers for generation). Experience with natural language processing (NLP) or computer vision. Contributions to open-source AI projects. Experience with distributed systems and big data technologies. Experience with prompt engineering. Location : - Remote

Key Responsibilities: Independently guide the technical direction and implementation by the whole team within defined architecture in all stages from conceptualization to deployment. Evaluate trade-offs between correctness, robustness, performance, and customer impact to ensure the development of the right solution, with client success at the forefront. Create and lead the team's technical documentation and repository management practices, including tasks such as creating branches, pull requests, merges, etc. Collaborate with product, design, and engineering teams to provide necessary oversight of architecture and dependencies influencing product strategy and direction. Contribute to code reviews, documentation, and addressing complex bug fixes with a focus on security, performance, and reliability. Be an active leader in the Engineering Practice community, mentoring Senior Engineers and others through Communities of Practice (CoPs) or on project teams, supporting the growth of technical capabilities. Minimum Qualifications: A minimum of 7+ years of experience/expertise working as a Python Engineer, with proficiency in the specified technologies. Python and Python frameworks Multithreaded/Multiprocessing System GoLang FastAPI, Pydentic AWS Azure Docker MLOps (Good to Have) High level of English proficiency required to interact with a globally-based development team. Experience leading Agile software development methodologies. Demonstrated experience following and adapting high-level architecture to project and client needs. Ability to verify/validate architecture implementations and influence overall architecture beyond the team. Expertise in applying object-oriented programming, with preferred experience in languages like Java/C#. High-level design proficiency following UML / C4 / ArchiMate. Experience in effectively working collaboratively among relevant information stakeholders to create and implement well-tested, scalable, secure, and performant enterprise-level systems that ultimately deliver the clients desired business outcome. Demonstrated initiative in mentoring other engineers and decision-makers throughout the organization. Very good knowledge of architectural styles and design patterns, SOLID principles and OWASP. Additional Experience Desired: Ability to set technical strategy and direct implementation across several teams/whole product. Ability to refine and clarify technical details (including definition of done) based on internal or external PO for User Stories and task assignments. Ability to provide technical orchestration among the overall tasks. Experience in building CI/CD pipelines. Knowledge of building Cloud Native applications. What is it like working for 3Pillar Global? At 3Pillar, we offer a world of opportunity: Imagine a flexible work environment – whether it's the office, your home, or a blend of both. From interviews to onboarding, we embody a remote-first approach. You will be part of a global team , learning from top talent around the world and across cultures, speaking English everyday. Our global workforce enables our team to leverage global resources to accomplish our work in efficient and effective teams. We’re big on your well-being – as a company, we spend a whole trimester in our annual cycle focused on wellbeing. Whether it is taking advantage of fitness offerings, mental health plans (country-dependent), or simply leveraging generous time off, we want all of our team members operating at their best. Our professional services model enables us to accelerate career growth and development opportunities - across projects, offerings, and industries. We are an equal opportunity employer. It goes without saying that we live by values like Intrinsic Dignity and Open Collaboration to create cutting-edge technology AND reinforce our commitment to diversity - globally and locally.

What You'll Do: Lead the development and deployment of NLP-based solutions to process and analyze unstructured data at scale. Design, train, and optimize machine learning models using libraries such as PyTorch, NLTK, and Scikit-learn. Architect and deploy AI/ML products on cloud platforms like Azure, GCP, or AWS. Collaborate with data engineering teams to ensure seamless integration of AI models into production systems. Perform advanced SQL analytics to extract actionable insights from structured datasets. Stay up-to-date with the latest advancements in NLP and machine learning techniques. Mentor junior data scientists and foster a culture of technical excellence within the team. Communicate complex technical concepts to non-technical stakeholders and customers. Partner with customers to understand their needs and translate them into technical solutions. What Were Looking For: Minimum 5 years of experience in data science, with a focus on NLP and unstructured data processing. Proven track record of launching NLP-driven products to live users. Expertise in Python and standard libraries such as PyTorch, NLTK, and Scikit-learn. Experience with Transformer-based models (e.g., BERT, GPT). Strong experience with one or more cloud platforms (Azure, GCP, AWS) for hosting and deploying AI/ML products. Familiarity with data engineering pipelines and best practices. Proficiency in SQL for analytics and data manipulation. Excellent problem-solving skills and ability to work with large-scale datasets. Strong interpersonal and communication skills, with the ability to mentor team members and interact with customers effectively. Preferred Skills: Knowledge of MLOps practices for model deployment and monitoring. Familiarity with tools like Airflow, Spark, or similar data processing frameworks. Background in working with customer-facing applications and APIs.

Role: Senior Solution Architect Computer Vision Experience: 6+ years Role Overview: The Senior Solution Architect will be responsible for designing, developing, and deploying large-scale, real-time vision solutions for object detection, pattern recognition, behavior analysis, and intelligence augmentation. This role demands a deep technical understanding of computer vision, machine learning, and AI infrastructure, combined with strong leadership capabilities. The architect will collaborate with cross-functional teams to translate business needs into robust technical solutions while ensuring efficiency, scalability, and innovation. Key Responsibilities: 1. Solution Architecture & Technical Leadership: Design and implement scalable, high-performance computer vision solutions for complex business challenges. Architect real-time image and video processing pipelines , integrating technologies for object detection, tracking, and behavior analysis. Develop fault-tolerant, distributed systems using state-of-the-art deep learning and image processing techniques. Drive end-to-end system optimization , including data pipelines, model inference efficiency, and GPU utilization . Evaluate and integrate cutting-edge AI frameworks, edge computing strategies, and cloud-based vision services . 2. Project Leadership & Execution: Define technical strategy and roadmap for delivering vision-based AI solutions. Own end-to-end project execution , from requirement gathering to deployment. Develop comprehensive solution blueprints, architecture documentation, and execution plans . Ensure alignment with business goals, cost-effectiveness, and system reliability . Set and manage stakeholder expectations , ensuring timely and successful project delivery. 3. Collaboration & Cross-Functional Engagement: Work closely with AI research teams, data engineers, software engineers, and product managers to align technical designs with business objectives. Collaborate with cloud infrastructure, DevOps, and MLOps teams to ensure smooth deployment and monitoring of vision pipelines. Partner with hardware teams for optimizing computer vision models for edge devices and embedded systems. 4. Mentorship & Team Development: Mentor and train junior and mid-level computer vision engineers to build strong technical capabilities. Establish and enforce best practices for AI model development, deployment, and monitoring . Foster a culture of innovation, knowledge sharing, and technical excellence within the team. Skills & Qualifications: Technical Expertise: Strong proficiency in Python and Linux , with expertise in writing optimized, fault-tolerant code . Deep understanding of image processing and computer vision techniques. Experience with libraries such as OpenCV, NVIDIA CUDA, Intel OpenVINO, TensorFlow, PyTorch, and NumPy . Expertise in machine learning, deep learning, and AI model optimization for real-time inference. Strong knowledge of MLOps, model monitoring, and lifecycle management . Experience with edge AI deployment and optimizations for embedded systems (Jetson, TPU, FPGA, etc.) . Soft Skills: Ability to translate complex business challenges into scalable AI solutions . Strong communication and collaboration skills across technical and non-technical teams. Proven leadership skills , including mentoring, team management, and decision-making. Innovative mindset , with a passion for staying ahead of the curve in AI and vision technologies. Preferred Experience: Prior experience in large-scale vision automation solutions , ideally in fast-paced startup environments . Track record of successfully architecting and deploying computer vision solutions from concept to production . Experience with edge computing, federated learning, and privacy-preserving AI techniques . Contributions to open-source AI/computer vision projects or relevant publications in the field.

Job Summary: Are you ready to join a game-changing open-source AI platform that harnesses the power of hybrid cloud to drive innovation? The Red Hat OpenShift AI team is looking for a Senior Software Engineer with Kubernetes and MLOps (Machine Learning Operations) experience to join our rapidly growing engineering team. Our focus is to create a platform, partner ecosystem, and community by which enterprise customers can solve problems to accelerate business success using AI. This is a very exciting opportunity to build and impact the next generation of MLOps platforms, participate in open source communities, contribute to the development of the OpenShift AI product, and be at the forefront of the exciting evolution of AI. Youll join an ecosystem that fosters continuous learning, career growth, and professional development. You will be contributing as a core developer for the Model Training team, the core Model training tools (Ray, Kubleflow, Pytorch etc) for OpenShift AI. You will work as part of an evolving development team to rapidly design, secure, build, test, and release new capabilities. This role is for an individual contributor who also leads other junior engineers in the team and collaborates closely with other developers and cross-functional teams. You will have the opportunity to actively participate in both our downstream efforts as well as the upstream projects. You should have a passion for working in open-source communities and for developing solutions that integrate Red Hat, open-source, and partner technologies into a cohesive platform. What you will do Participate in architect and lead implementation tasks of the new features and solutions for OpenShift AI Innovate in the MLOps domain by contributing meaningfully to upstream communities Develop integrations between various portions of the OpenShift AI stack Participate in technical vision and leadership on critical and high impact projects Ensure non-functional requirements including security, resiliency, and maintainability are met Write unit and integration tests and work with quality engineers to ensure product quality Use CI/CD best practices to deliver solutions as productization efforts into OpenShift AI Contribute to a culture of continuous improvement by sharing recommendations and technical knowledge with team members Collaborate with product management, other engineering and cross-functional teams to analyze and clarify business requirements Communicate effectively to stakeholders and team members to ensure proper visibility of development efforts Give thoughtful and prompt code reviews Help in mentoring, influencing, and coaching a distributed team of engineers What you will bring Experience developing applications in Go Experience developing applications in Python Experience developing applications in Kubernetes, OpenShift, or other cloud-native technologies Ability to quickly learn and guide others on using new tools and technologies Proven ability to innovate and a passion for staying at the forefront of technology. Experience with distributed systems (especially those that run on Kubernetes) and troubleshooting them Autonomous work ethic, thriving in a dynamic, fast-paced environment. Experience providing technical leadership in a global team and delivering on a vision Excellent written and verbal communication skills The following will be considered a plus: While a Bachelors degree or higher in computer science or a related discipline is valued, we prioritize practical experience and technical prowess Understanding of how Open Source communities work Experience with development for public cloud services (AWS, GCE, Azure) Experience working with or deploying MLOps platforms Experience with AI/ML Model training and tuning Experience writing Kubernetes/OpenShift controllers and operators Experience writing DSLs in Python or other language

5+ years of experience As the Infrastructure and Ops Engineer, you will work on operations related our UAIS AI Studio (enterprise AI/ML platform), and in particular in relation to AI/ML training initiative supporting thousands of learners on the platform. This individual contributor (IC) role requires experience on working on large-scale AI/ML platforms guaranteeing stability, reliability, scalability, and performance. Experience with modern Infrastructure and DevOps tools and paradigms, as well as hands-on knowledge with major cloud-based services like Azure, AWS and GCP is a must. Primary Responsibilities: Continuous support: Provide continuous SRE support to thousands of geographically distributed learners on the UAIS platform: respond to tickets, triage support, liaise with customers. Automation & DevOps: Improve existing Infrastructure as Code (IaC) according to best DevOps practices. Systems Monitoring: Develop and maintain monitoring frameworks for UAIS infrastructure in relation to AI/ML training program Security & Compliance: Collaborate with cybersecurity teams to ensure all systems and operations comply with industry standards and are secure against evolving threats. Capacity Planning & Cost Optimization: Forecast and manage capacity requirements for the AI/ML training environment, while identifying opportunities to reduce costs without compromising performance. Required Qualifications: Bachelors degree in computer science, information technology, or a related field. 5+ years of infrastructure experience: Proven experience working on large-scale, cloud-based, enterprise-level software platforms and deep understanding of multi-cloud architectures, specifically Azure, AWS, and GCP, with hands-on experience in cloud management. 3+ years of practical experience in Infrastructure-as-Code and CI/CD tools like Terraform, Git Actions and alike. 2+ years of practical experience in containerization technologies (Kubernetes, Docker) and orchestration 2+ years of practical experience in Scripting & Automation Skills: Advanced proficiency in scripting languages such as Python and Bash to support automation and system integration efforts. Preferred Qualifications: Security & Compliance Knowledge: Strong understanding of security best practices and experience ensuring compliance with relevant regulatory frameworks. Machine Learning and LLM Operations: Exposure to modern tools and techniques in MLOps and LLMOps fields. Exposure to AI/ML-specific infrastructure tools (e.g., MLflow, Kubeflow) for managing and deploying models at scale. Exposure to a Regulated Industry: Experience working within a healthcare or regulated industry, with solid understanding of the unique challenges and compliance requirements. Ability to work independently, manage multiple projects simultaneously, and adapt to changing priorities in a fast-paced environment. Location- Anywhere in onsite, Delhi NCR, Bangalore, Chennai, Pune, Kolkata, Ahmedabad, Mumbai, Hyderabad

About the Role: We are seeking a highly experienced and visionary AI Senior Technical Lead to drive the technical direction and execution of our AI initiatives. This role is pivotal in shaping our AI strategy, leading a team of talented AI engineers, and ensuring the delivery of innovative and impactful AI solutions. As a technical leader, you will be responsible for defining architectural vision, driving best practices, and fostering a culture of technical excellence across diverse AI domains. Responsibilities: Technical Leadership & Strategy: Define and communicate the technical vision and strategy for AI projects. Lead the architectural design and implementation of complex AI systems. Evaluate and recommend emerging AI technologies and methodologies. Drive the adoption of best practices for AI development, deployment, and maintenance. Contribute to the strategic planning of AI initiatives, aligning them with business goals. Team Leadership & Mentorship: Lead and mentor a team of AI engineers, providing technical guidance and support. Foster a collaborative and innovative team environment. Conduct code reviews and ensure code quality. Identify and address skill gaps within the team. Facilitate knowledge sharing and continuous learning. AI System Development & Implementation: Oversee the development and deployment of scalable and robust AI solutions. Ensure the performance, reliability, and security of AI systems. Drive the development of AI pipelines and infrastructure. Lead the integration of AI models with existing systems and applications. Troubleshoot and resolve complex technical issues. Research & Innovation: Stay abreast of the latest advancements in AI and machine learning. Identify opportunities to apply AI to solve complex business challenges, including opportunities within Generative AI. Drive research and development efforts to explore new AI technologies. Evaluate and prototype new AI solutions. Collaboration & Communication: Collaborate with cross-functional teams, including product managers, data scientists, and software engineers. Communicate complex technical concepts to both technical and non-technical audiences. Provide technical presentations and demonstrations. Document technical designs, code, and processes. Required Skills and Qualifications: Education : Batchelors or Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: 8+ years of experience in AI/ML development, with a proven track record of leading complex AI projects. Extensive experience in designing and implementing scalable AI systems. Proven experience in leading and mentoring technical teams. Deep understanding of machine learning and deep learning algorithms and architectures. Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services. Technical Skills: Expertise in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Strong understanding of AI infrastructure and deployment strategies. Experience with MLOps best practices. Experience with vector databases, and LLM implementations. Proficiency in software development principles and best practices. Experience with containerization and orchestration tools (e.g., Docker, Kubernetes). Soft Skills: Exceptional leadership and communication skills. Strong problem-solving and analytical skills. Ability to think strategically and drive innovation. Excellent interpersonal and collaboration skills. Strong presentation skills. Preferred Qualifications: Experience with Generative AI technologies and methodologies (e.g., GANs, Diffusion Models, Transformers for generation). Experience with natural language processing (NLP) or computer vision. Contributions to open-source AI projects. Experience with distributed systems and big data technologies. Experience with prompt engineering. 8+ years of experience in AI/ML development Location : - Remote

Immediate joiners preferred/Serving NP preferred Mandate skills- MLOPS, End - End Deployment,Data pipeline,Machine learning,Model building,Model development 6+ years exp of which 3+ years of relevant data science experience and at least 3 years of software development experience. Experience in devising creative analytical approaches to solve business problems Developing and enhancing algorithms and models to solve business problem Maintaining all models along with development and updating of code and process documentation Designing a solution approach, leading a team to deploy and maintain it in production. Proficient in Structured programming language is a must - such as Python, C/C++, Java is mandatory. Cloud concepts and significant hands-on experience with at least one cloud provider. Should be knowledgeable about the pros and cons of the various services and comfortable discussing the tradeoffs with stakeholders. Strong SQL skills. Good knowledge of Data science approaches, machine learning algorithms and statistical methods. ETL and Data Engineering pipelines using Spark/PySpark. Workflow orchestration tools like Airflow.

1. End-to-End ML Solutions: Take full ownership of developing end-to-end machine learning solutions within the geospatial domain, focusing on image data, point cloud data, geophysical data, geological data, and related datasets, within the GIS context and ensuring alignment with customer needs and company objectives. 2. Technical Collaboration: Collaborate closely with development teams, translating geospatial requirements into technical specifications for seamless integration with the product. 3. Data Preprocessing: Develop and refine preprocessing pipelines for input data, ensuring data quality and normalization. 4. Continuous Learning: Stay updated on the latest advancements in machine learning and GIS technology, actively participating in training programs and self-directed learning. 5. Documentation and Reporting: Document progress, methodologies, and outcomes for knowledge transfer and future reference. 1+ years of hands-on experience in machine learning engineering or related technical roles. Proven ability to engage with technical teams, translating geospatial requirements into machine learning specifications. 7. Strong background in algorithm development, model training, and deployment. Experience with GIS related data is a plus. Expertise in Python and machine learning frameworks (e.g., TensorFlow, PyTorch). 8. Solid understanding of image analysis, and point cloud processing. Excellent analytical and problem-solving skills, with a data-driven decision-making approach. Effective verbal and written communication skills, capable of conveying complex technical concepts clearly. 9. Demonstrated ability to work collaboratively with cross-functional teams, including development, design, and marketing. Comfortable working in a fast-paced environment, adaptable to changing technical priorities and requirements

Experience in Deep learning engineering (mostly on MLOps) Strong NLP/LLM experience and processing text using LLM Proficient in Pyspark/Data bricks & Python programming. Building backend applications (data processing etc.) using Python and Deep learning frame works. Deploying models and building APIS (FAST API, FLASK API) Need to have experience working with GPUS. Working knowledge of Vector databases like 1) Milvus 2) azure cognitive search 3) quadrant etc Experience in transformers and working with hugging face models like llama, Mixtral AI and embedding models etc. Good to have: Knowledge and experience in Kubernetes, docker etc. Cloud Experience working with VMS and azure storage. Sound data engineering experience.

Project Description: Grid Dynamics wants to build a centralized, observable and secure platform for their ML, Computer Vision, LLM and SLM models. Grid Dynamics wants to onboard a vast number of AI agents, able to cover multiple required skills, ensuring a certain level of control and security in regards to their usage and availability. The observable platform must be vendor-agnostic, easy to extend to multiple type of AI applications and flexible in terms of technologies, frameworks and data types. This project is focused on establishing a centralized LLMOps capability where every ML, CV, AI-enabled application is monitored, observed, secured and provides logs of every activity. The solution consists of key building blocks such monitor every step in a RAG, Multimodal RAG or Agentic Platform, track performances and provide curated datasets for potential fine-tuning. Alignment with business scenarios, PepVigil provides also certain guardrails that allow or block interactions user-to-agent, agent-to-agent or agent-to-user. Also, Guardrails will enable predefined workflows, aimed to give more control over the series of LLM chains. Details on Tech Stack Job Qualifications and Skill Sets Advanced degree in Data Science, Computer Science, Statistics, or a related field Setting up Agent Mesh (LangSmith) Setting up Agent communication protocols (JSON/XML etc) Setting up message queues, CI/CD pipelines (Azure Queue Storage, Azure DevOps) Setting up integrations Langgrah, LangFuse Knowledge on Observability tool Arize-Phoenix tools Managing Agent Registry, Integrating with AgentAuth framework like Composio Setting up AgentCompute (Sandpack, E2BDev, Assistant APIs) Integration with IAM (Azure IAM, OKTA) Performing/Configuring Dynamic Orchestration and agent permissions Tech Stack Required: ML MLOPs Agent (Agent / Agent Mesh) LangFuse, LanChain, LangGraph Deployments (Docker, Jenkins, Kubernetes) Cloud Platforms: Azure/AWS/GCP We offer: Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office

Exp in SQL, Python with all data science and machine learning libraries, deep learning algorithms, MLOPs,XGBoost, GBM, Generative AI to enable state-of-the-art agents and algorithms to improve the product. 3-12 years of experience in data science

4- 6 years of experience as AI/ML engineer or similar role. Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Hands-on experience with model development and deployment processes. Proficiency in programming languages such as Python. Experience with data preprocessing, feature engineering, and model evaluation techniques. Familiarity with cloud platforms (e.g., AWS) and containerization (e.g., Docker, Kubernetes). Familiarity with version control systems (e.g., GitHub). Excellent problem-solving skills and attention to detail. Strong communication skills and the ability to work collaboratively in a team environment. Proficiency in data manipulation and analysis using libraries such as NumPy and Pandas. Good to have knowledge of deep learning, ML Ops: Kubeflow, MLFlow, Nextflow.

Profile : We are looking for an experienced and high-energy ML Ops Engineer. The primary function of this role is to design enterprise architecture. Envision and drive solution architecture after hearing the product s vision and user stories with ability to envision and drive a proactive architectural roadmap for an existing product keeping in mind the future requirements. Requirements : - Experience building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer (or equivalent). - Hands-on expertise in Python and ML frameworks. - Expertise with Linux administration. - Experience working with cloud computing and database systems. - Experience building custom integrations between cloud-based systems using APIs. - Experience developing and maintaining ML systems built with open source tools. - Experience developing with containers and Kubernetes in cloud computing environments. - Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.). - Ability to translate business needs to technical requirements. - Strong understanding of software testing, benchmarking, and continuous integration. - Exposure to machine learning methodology and best practices. - Experience with Prometheus and Grafana integrations for highly scalable environments. Responsibilities : - Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale. - Take offline models data scientists build and turn them into a real machine learning production system. - Develop and deploy scalable tools and services to handle machine learning training and inference. - Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems. - Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc. - Support model development, with an emphasis on auditability, versioning, and data security. - Facilitate the development and deployment of proof-of-concept machine learning systems.

The Data Science Presales Manager is responsible for leading the presales efforts, developing strategies to drive sales growth, and ensuring the successful alignment of client needs with our solutions. This role requires strong technical knowledge, exceptional communication skills, and the ability to work collaboratively with sales, product, and engineering teams. The ideal candidate will understand client requirements and architect tailored solutions that showcase our offerings' value. Technical Skills: AI/ML Fundamentals Understanding of machine learning models, deep learning, NLP, computer vision, and GenAI. Cloud Platforms Experience with Azure, AWS, or Google Cloud AI/ML services. Data Engineering Basics Knowledge of data preprocessing, feature engineering, and model deployment. MLOps & Model Deployment Familiarity with CI/CD pipelines for AI models. Programming Python, SQL, and libraries like TensorFlow, PyTorch, and LangChain. Solution Architecture Ability to design AI-driven solutions tailored to business problems. API Integration Understanding REST APIs, SDKs, and integrating AI solutions with existing systems. Business & Soft Skills: Presales & Proposal Writing Crafting proposals, responding to RFPs/RFIs, and creating proof of concepts (POCs). Client Engagement Understanding client pain points and translating them into AI solutions. Presentation & Storytelling Ability to present technical concepts to both technical and non-technical stakeholders. Industry Awareness Knowledge of AI use cases across industries (e.g., finance, healthcare, retail, etc.). Competitive Analysis Understanding AI trends and how competitors are positioning their solutions. Collaboration & Teamwork Working closely with sales, product, and engineering teams to deliver solutions.

At Roche, were driven by a shared passion for transforming healthcare with innovative solutions, and we’re looking for a Senior Data Engineer with a deep passion for Machine Learning Operations (MLOps) to join our team. As a part of the Analytics & Insights Product Line , you will work in a multidisciplinary team, supporting the growth of our platform and helping to revolutionize the field of MLOps. Your Opportunity: Research and implement MLOps tools, frameworks, and platforms for Data Science projects. Contribute to increasing MLOps maturity within the organization through a structured backlog of activities. Introduce agile and automated approaches to Data Science processes. Lead internal training and presentations on MLOps tools and their benefits. Who You Are: 7+ years of hands-on MLOps experience. Strong experience with Kubernetes . Demonstrated experience in operationalizing Data Science projects using popular MLOps frameworks such as Kubeflow or AWS SageMaker . A solid understanding of ML and AI concepts and hands-on experience with ML model development. Proficient in Python for both machine learning tasks and automation. Good knowledge of Bash and Unix command-line tools. Experience with DevOps , CI/CD/CT , and pipeline implementation . Familiar with AWS , with knowledge of other cloud providers considered a plus. Exposure to LLMOps and genAI technologies. Strong communication skills, with experience in running project teams. Why Join Us: This is an excellent opportunity for an experienced MLOps professional to advance their career by joining a global leader in biotechnology, working on cutting-edge technologies that have a direct impact on improving patient care. We offer a collaborative and inclusive environment where you can grow your technical skills and contribute to building innovative solutions. Interested in joining our team? Apply today to make an impact with Roche!