AI Principal

HCLTech is looking for a highly talented and self- motivated AI Principal to join it in advancing the technological world through innovation and creativity. Job Title: AI PrincipalJob ID: 16157Position Type: Full-timeLocation: Remote, in USA with travel Role/ResponsibilitiesHands-on Technical Leadership & Oversight: • Architecting Scalable Systems: Lead the design of AI, GenAI solutions, machine learning pipelines, and data architectures that ensure performance, scalability, and resilience. • Hands-on Development: Actively contribute to coding, code reviews, solution design, and hands-on troubleshooting for critical components of GenAI, ML, and data pipelines. • Cross-Functional Collaboration: Work with Account Teams, Client Partners, and Domain SMEs to ensure alignment between technical solutions and business needs. • Team Leadership: Mentor and guide engineers across various functions including AI, GenAI, Full Stack, Data Pipelines, DevOps, and Machine Learning, fostering a collaborative and high-performance team environment.Solution Design & Architecture: • System & API Architecture: Design and implement microservices architectures, RESTful APIs, cloud-based services, and machine learning models that integrate seamlessly into GenAI and data platforms. • AI, GenAI, Agentic AI Integration: Lead the integration of AI, GenAI, and Agentic applications, NLP models, and large language models(e.g., GPT, BERT) into scalable production systems. • Data Pipelines: Architect ETL pipelines, data lakes, and data warehouses using industry-leading tools like Apache Spark, Airflow, and Google BigQuery. • Cloud Infrastructure: Drive the deployment and scaling of solutions using cloud platforms like AWS, Azure, GCP, and other relevant cloud-native technologies.Machine Learning & AI Solutions: • ML Integration: Lead the design and deployment of machine learning models using frameworks like PyTorch, TensorFlow, scikit-learn, and spaCy into end-to-end production workflows, including building of SLMs. • Prompt Engineering: Develop and optimize prompt engineering techniques for GenAI models to ensure accurate, relevant, and reliable output. • Model Monitoring: Implement best practices for ML model performance monitoring, continuous training, and model versioning in production environments.DevOps & Cloud Infrastructure: • CI/CD Pipeline Leadership: Have good working knowledge of CI/CD pipelines, leveraging tools like Jenkins, GitLab CI, Terraform, and Ansible for automating the build, test, and deployment processes. • Infrastructure Automation: Lead efforts in Infrastructure-as-Code and ensure automated provisioning of infrastructure through tools like Terraform, CloudFormation, Docker, and Kubernetes. • Cloud Management: Ensure robust integration with cloud platforms such as AWS, Azure, GCP, and experience with specific services such as AWS Lambda, Azure ML, Google BigQuery, and others.Cross-Team Collaboration: • Stakeholder Communication: Act as the key technical liaison between engineering teams and non-technical stakeholders, ensuring technical solutions meet business and user requirements. • Agile Development: Promote Agile methodologies, do solution, and code design reviews to deliver milestones efficiently while ensuring high-quality code.Performance Optimization & Scalability: • Optimization: Lead performance tuning and optimization for high-traffic applications, especially around machine learning models, data storage, ETL processes, and API latency. • Scaling: Ensure solutions scale seamlessly with growth, leveraging cloud-native tools and load balancing strategies such as AWS Auto Scaling, Azure Load Balancer, Kubernetes Horizontal Pod Autoscaler. Qualifications & Experience Minimum Requirements • 13+ years of hands-on technical experience in software engineering, with at least 5+ years in a leadership role managing cross-functional teams, including AI, GenAI, machine learning, data engineering, and cloud infrastructure. • Hands-on Experience in designing and developing large-scale systems, including AI, GenAI, Agentic AI, API architectures, data systems, ML pipelines, and cloud-native applications. • Strong experience with cloud platforms such as AWS, GCP, Azure with a focus on cloud services related to ML, AI, and data engineering. • Programming Languages: Proficiency in Python, Flask/Django/FastAPI • Experience with API development (RESTful APIs, GraphQL). • Machine Learning & AI: Extensive experience in building and deploying ML models using TensorFlow, PyTorch, scikit-learn, and spaCy, with hands-on experience in integrating them into AI, GenAI and Agentic frameworks like LangChain and MCP. • Data Engineering: Familiarity with ETL pipelines, data lakes, data warehouses (e.g., AWS Redshift, Google BigQuery, PostgreSQL), and data processing tools like Apache Spark, Airflow, and Kafka. • DevOps & Automation: Strong expertise in CI/CD pipelines, containerization (Docker, Kubernetes), Infrastructure-as-Code (Terraform,