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As Ford Motor Company embarks on a significant multi-year Platform Lifecycle Management (PLM) program to modernize critical IT applications across the enterprise, you have a unique opportunity to join the team as a GenAI Technical Manager (LL6). In this role, you will play a pivotal part in driving the practical adoption of Generative AI (GenAI) at Ford, with a focus on creating accelerators for the PLM modernization effort and enhancing the Ford Developer Experience (DX). Your expertise will be crucial in leading the technical development and implementation of GenAI solutions within this strategic program. You will collaborate closely with various teams including PLM program leaders, GDIA (Global Data Insight & Analytics), architecture teams, PDOs (Product Driven Organizations), and engineering teams to design, build, and deploy cutting-edge GenAI tools and platforms. Your responsibilities will include leading the technical design, development, testing, and deployment of GenAI solutions, translating GenAI strategy into actionable projects, managing the technical lifecycle of GenAI tools, and overseeing the integration of GenAI capabilities into existing workflows and processes. As a technical expert in GenAI models and frameworks, you will provide guidance to development teams, architects, and stakeholders on best practices, architecture patterns, security considerations, and ethical AI principles. You will stay updated on the evolving GenAI landscape, evaluate new tools and models, and lead the development of GenAI-powered accelerators and tools to automate and streamline processes within the PLM program. Collaboration and stakeholder management will be key aspects of your role, requiring effective communication of complex technical concepts to diverse audiences. You will also lead proof-of-concept projects with emerging GenAI technologies, champion experimentation and adoption of successful tools and practices, and mentor junior team members in GenAI development tasks. To qualify for this role, you should have a Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field, along with 8-10+ years of experience in software development/engineering with a focus on AI/ML and Generative AI solutions. Deep practical expertise in GenAI, strong software development foundation, and familiarity with enterprise application context are essential qualifications. Preferred qualifications include GCP certifications, experience with Agile methodologies, and familiarity with PLM concepts or the automotive industry. If you are passionate about innovation in the AI space, possess strong analytical and strategic thinking skills, and excel in a fast-paced, global environment, we invite you to join us in shaping the future of AI at Ford Motor Company.,

Position Title: AI/ML Engineer Company: Cyfuture India Pvt. Ltd. Industry: IT Services and IT Consulting Location: Sector 81, NSEZ, Noida (5 Days Work From Office) Website: www.cyfuture.com About Cyfuture Cyfuture is a trusted name in IT services and cloud infrastructure, offering state-of-the-art data center solutions and managed services across platforms like AWS, Azure, and VMWare. We are expanding rapidly in system integration and managed services, building strong alliances with global OEMs like VMWare, AWS, Azure, HP, Dell, Lenovo, and Palo Alto. Position Overview We are hiring an experienced AI/ML Engineer to lead and shape our AI/ML initiatives. The ideal candidate will have hands-on experience in machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions. This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems. Key Responsibilities Lead and mentor a high-performing AI/ML team. Design and execute AI/ML strategies aligned with business goals. Collaborate with product and engineering teams to identify impactful AI opportunities. Build, train, fine-tune, and deploy ML models in production environments. Manage operations of LLMs and other AI models using modern cloud and MLOps tools. Implement scalable and automated ML pipelines (e.g., with Kubeflow or MLRun). Handle containerization and orchestration using Docker and Kubernetes. Optimize GPU/TPU resources for training and inference tasks. Develop efficient RAG pipelines with low latency and high retrieval accuracy. Automate CI/CD workflows for continuous integration and delivery of ML systems. Key Skills & Expertise 1. Cloud Computing & Deployment Proficiency in AWS, Google Cloud, or Azure for scalable model deployment. Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML. Expertise in Docker and Kubernetes for containerized deployments Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation. 2. Machine Learning & Deep Learning Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost. Experience with MLOps tools for integration, monitoring, and automation. Expertise in pre-trained models, transfer learning, and designing custom architectures. 3. Programming & Software Engineering Strong skills in Python (NumPy, Pandas, Matplotlib, SciPy) for ML development. Backend/API development with FastAPI , Flask , or Django . Database handling with SQL and NoSQL (PostgreSQL, MongoDB, BigQuery). Familiarity with CI/CD pipelines (GitHub Actions, Jenkins). 4. Scalable AI Systems Proven ability to build AI-driven applications at scale. Handle large datasets, high-throughput requests, and real-time inference. Knowledge of distributed computing: Apache Spark, Dask, Ray . 5. Model Monitoring & Optimization Hands-on with model compression, quantization, and pruning . A/B testing and performance tracking in production. Knowledge of model retraining pipelines for continuous learning. 6. Resource Optimization Efficient use of compute resources: GPUs, TPUs, CPUs . Experience with serverless architectures to reduce cost. Auto-scaling and load balancing for high-traffic systems. 7. Problem-Solving & Collaboration Translate complex ML models into user-friendly applications. Work effectively with data scientists, engineers, and product teams. Write clear technical documentation and architecture reports . Udisha Parashar Senior Talent Acquisition Specialist Mob: +91- 9301895707 Email: udisha.parashar@cyfuture.com URL: www.cyfuture.com