Job Title: AI/ML Lead – Generative AI & LLMs
Location: Gurgaon
Employment Type: Full-time
Experience Level: 6+Years
Key Responsibilities
- AI Model Development: Design, train, and fine-tune generative AI models, including LLMs and Retrieval-Augmented Generation (RAG) models, to solve real-world business problems.
- Prompt Engineering: Create, test, and optimize prompts to improve the relevance, coherence, and usefulness of model outputs.
- Cloud Deployment: Deploy and manage AI/ML solutions on cloud platforms such as AWS, Azure, or Google Cloud, ensuring performance, scalability, and cost-efficiency.
- Data Management: Partner with data scientists and data engineers to enable efficient data ingestion, preprocessing, and feature engineering pipelines.
- Model Evaluation & Optimization: Monitor model performance, run experiments, and iterate to enhance accuracy, latency, and resource efficiency.
- Innovation: Stay current with trends in AI, LLMs, generative models, and cloud infrastructure. Evaluate and integrate emerging tools and techniques.
- Cross-functional Collaboration: Work closely with product managers, software engineers, and business stakeholders to align AI initiatives with product goals.
- Documentation: Maintain thorough documentation of models, workflows, experiments, and deployment processes to ensure reproducibility and collaboration.
Experience:
- Demonstrated experience developing and deploying AI/ML models, especially in Generative AI, LLMs, or RAG architectures.
- Hands-on experience working with one or more major cloud platforms (AWS, Azure, GCP).
- Experience with prompt engineering and optimization techniques for large language models.
Technical Skills:
- Proficient in Python and AI/ML frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.
- Strong understanding of NLP techniques, machine learning algorithms, and data preprocessing pipelines.
- Experience with containerization and orchestration tools like Docker and Kubernetes is a plus.
- Familiarity with MLOps practices, CI/CD pipelines, and tools for automated model monitoring and deployment.
