GenAI Senior Solution Director || Bangalore / Chennai / Noida / Hyderabad

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 .