HCLTech Hiring! Full Time Senior Solution Director in - Ricebowl

Job Summary

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.

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.

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  Hands-on Development: Actively contribute to coding, code reviews, solution design, and hands-on troubleshooting for critical components of GenAI, ML, and data pipelines.

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  Cross-Functional Collaboration: Work with Account Teams, Client Partners and Domain SMEs to ensure alignment between technical solutions and business needs.

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  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.

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  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.

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  Data Pipelines: Architect ETL pipelines, data lakes, and data warehouses using industry-leading tools like Apache Spark, Airflow, and Google BigQuery.

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  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.

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  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.

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  Infrastructure Automation: Lead efforts in Infrastructure-as-Code and ensure automated provisioning of infrastructure through tools like Terraform, CloudFormation, Docker, and Kubernetes.

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  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.

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  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.

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  Scaling: Ensure solutions scale seamlessly with growth, leveraging cloud-native tools and load balancingstrategies such as AWS Auto Scaling, Azure Load Balancer, Kubernetes Horizontal Pod Autoscaler.

Skill Requirements

• 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.

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  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.

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  Programming Languages: Proficiency in Python, Flask/Django/FastAPI

• Experience with API development (RESTful APIs, GraphQL).

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  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.

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  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.

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  MLOps: Experience with MLOps tools and practices for model monitoring, deployment, and continuous training in production environments.

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  Machine Learning Model Optimization: Understanding of techniques for hyperparameter tuning, model interpretability, and model versioning.

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  Business Intelligence (BI): Experience with BI tools such as Tableau, Power BI, and data visualization techniques.

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  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.

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  Data Engineering: Apache Kafka, Apache Spark, Airflow, Presto, Hadoop, Google BigQuery, AWS Redshift.

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  Machine Learning: TensorFlow, PyTorch, scikit-learn, spaCy, HuggingFace, OpenAI GPT.

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  CI/CD & DevOps: GitLab CI, Jenkins, Docker, Kubernetes, Terraform, Ansible, Helm.

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  API Frameworks: FastAPI, Flask, GraphQL, RESTful APIs.

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  Version Control: Git, GitHub, GitLab.