Principal GenAI Engineer
- HCLTech
- 7 - 12 years
- Noida, Gurugram
- 11 days ago
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Job Description
Role & responsibilities :
Principal GenAI Engineer
A Principal GenAI Engineer leads the design, development, and implementation of artificial intelligence solutions that address specific business problems. Individuals in this role have extensive experience with machine learning models, natural language processing, computer vision, or other AI technologies. They collaborate closely with data scientists, data engineers, and cloud engineers to deliver AI solutions that drive business value.
Key Tasks & Responsibilities
1) Deep Learning & Transformer Architecture
a) Develop a solid understanding of neural networks and their applications in various AI tasks.
b) Design and implement transformer architectures for diverse applications, including natural language processing (NLP) and computer vision.
c) Explore and experiment with multi-modal diffusion architectures as a good-to-have component.
2) LLM Development & Implementation
a) Build and fine-tune large language models (LLMs) for specific use cases, including document comparison, chunking, and retrieval-augmented generation (RAG).
b) Develop and enhance chatbot and NLP-based applications to improve user interactions and experiences.
c) Integrate embedding models with vector databases, utilizing tools such as FAISS, Pinecone, and AWS OpenSearch.
3) Multi-Agent & Rules-Based AI Implementation
a) Implement rules-based AI logic within applications to enhance decision-making processes.
b) Develop and orchestrate multi-agent AI systems to solve complex problems efficiently.
c) Utilize frameworks like LangChain, LangGraph, and LlamaIndex (GPT Index) to create and manage workflows that chain multiple LLMs together.
4) AI Tools & Libraries
a) Leverage AWS AI services, such as Amazon Bedrock and SageMaker, to build and deploy AI solutions.
b) Utilize libraries and APIs from Hugging Face and OpenAI to enhance model capabilities and performance.
5) Cloud & DevOps (AWS Focused)
a) Deploy AI models using AWS services like Fargate, EKS, and ECS, ensuring scalability and reliability.
b) Implement continuous integration and continuous deployment (CI/CD) pipelines for AI models using AWS CodeBuild, CodePipeline, and Lambda Functions.
c) Work with containerized AI services using Docker and Kubernetes to streamline development and deployment processes.
6)Model Deployment & API Integration
a) Demonstrate hands-on Python experience by delivering at least two AI projects from inception to production.
b) Develop and integrate APIs (REST APIs, GraphQL, AWS API Gateway) to facilitate communication between AI models and applications.
c) Reuse and customize GitHub-based AI repositories, adapting them for production use and ensuring high-quality code standards.
Preferred candidate profile
Qualifications & Competencies
Required:
- Bachelors or Masters degree in Computer Science, Data Science, or a related field.
- 8+ years of experience in AI / MI roles with the required skills.
- Basics of Deep Learning Transformer Architecture
- Understanding of Neural Networks
- Transformer Architecture and Applications
- MultiModal Diffusion Architecture (Good to have)
- LLM Development & Implementation
- Hands-on experience in building and fine-tuning LLMs for:
- Document comparison & chunking
- Retrieval-Augmented Generation (RAG)
- Chatbot & NLP-based applications
- Experience in embedding models and vector database integration (FAISS, Pinecone, AWS OpenSearch).
- Multi-Agent & Rules-Based AI Implementation
- Implementing rules-based AI logic in applications.
- Developing multi-agent AI systems with orchestration.
- Hands-on with LangChain, LangGraph, LlamaIndex (GPT Index) for chaining multiple LLM workflows.
- AI Tools & Libraries
- Strong knowledge of AWS AI Services:
- Amazon Bedrock, SageMaker etc
- Hands-on with Hugging Face, OpenAI API, GPT models.
- Cloud & DevOps (AWS Focused)
- Deploying AI models using AWS Fargate, EKS, ECS.
- Implementing CI/CD for AI models using AWS CodeBuild, CodePipeline, Lambda Functions.
- Experience in containerized AI services using Docker & Kubernetes.
- Model Deployment & API Integration
- Hands-on Python experience with at least two delivered AI projects.
- API development and integration: REST APIs, GraphQL, AWS API Gateway.
- Reusing and customizing GitHub-based AI repositories for production use.
Preferred:
- Relevant certifications in AI (e.g., AWS Certified Cloud / AI practitioner, Azure Fundamentals)
- Experience with multi-modal architectures and advanced AI techniques.
- Knowledge of AI ethics and governance practices.
- Familiarity with Agile methodologies and project management tools.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: NLP / DL Engineering / ArchitectEmployement Type: Full timeContact Details:
Company: HCLTechLocation(s): Noida, Gurugram
Keyskills: Agentic Ai Generative Ai Artificial Intelligence Generative Artificial Intelligence Python Ai Techniques Ai Platform
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