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HCLTech is hiring GenAI Senior Solution Director Job Title: Senior Solution Director / Data and AI Principal – AI, GenAI, and Analytics (E5 and Above) Job Overview: We are seeking an experienced Senior Solution Director, who will play a pivotal role in architecting , leading , and actively contributing to the development of AI, GenAI and Analytics applications, machine learning models , and cloud-native infrastructure . This hands-on leadership position requires extensive technical expertise and experience in managing a diverse, cross-functional team of engineers spanning GenAI App Development, Data Science , Machine Learning , Full Stack , DevOps , Cloud Infrastructure , and API development . You will be responsible for shaping new opportunities, architecting complex systems , making critical decisions, and leading teams to deliver high-quality, scalable solutions while remaining directly involved in coding , technical design , and problem-solving . Overall Experience: 12 to 19 yrs Location : Bangalore/Chennai/Noida/Hyderabad Notice Period: Immediate/30 days Key Responsibilities: Hands-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 and 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 . Required Qualifications: 15+ 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 , CloudFormation , Ansible ). Experience with API security , OAuth , and rate limiting for high-traffic, secure systems. Desirable Skills: Big Data & Distributed Systems : Knowledge of Hadoop , Spark , Presto , and other big data technologies for distributed processing. MLOps : Experience with MLOps tools and practices for model monitoring , deployment , and continuous training in production environments. Machine Learning Model Optimization : Understanding of techniques for hyperparameter tuning , model interpretability , and model versioning . Business Intelligence (BI) : Experience with BI tools such as Tableau , Power BI , and data visualization techniques. Security & Compliance : Familiarity with security best practices for cloud-native applications and regulatory compliance (e.g., GDPR , HIPAA ). Tools & Technologies: Cloud Platforms : AWS , GCP , Azure , Google Cloud AI , AWS SageMaker , Azure Machine Learning . Data Engineering : Apache Kafka , Apache Spark , Airflow , Presto , Hadoop , Google BigQuery , AWS Redshift . Machine Learning : TensorFlow , PyTorch , scikit-learn , spaCy , HuggingFace , OpenAI GPT . CI/CD & DevOps : GitLab CI , Jenkins , Docker , Kubernetes , Terraform , Ansible , Helm . API Frameworks : FastAPI , Flask , GraphQL , RESTful APIs . Version Control : Git , GitHub , GitLab .