Key Responsibilities
- Model Development & Training:
- Develop, train, and optimize ML and generative AI models including LLMs, transformers, diffusion models, and embedding-based models.
- Generative AI Applications:
- Build real-world generative AI applications such as chatbots, RAG systems, summarization, content generation, and agent frameworks.
- Prompt Engineering & LLM Integration:
- Apply prompt design, tuning, and evaluation techniques using tools like LangChain, LlamaIndex, or similar frameworks.
- Data Pipeline & MLOps:
- Build scalable ML pipelines for data ingestion, feature engineering, model deployment, and monitoring using MLOps best practices.
- Cloud & Infrastructure:
- Deploy models and services on AWS / GCP / Azure, leveraging containerization (Docker/Kubernetes) and GPU infrastructure.
- Research & Innovation:
- Stay current with AI research, experiment with new models, and contribute ideas for enhancements and automation.
- Documentation & Collaboration:
- Maintain clear technical documentation and collaborate with cross-functional engineering and product teams.
Required Skills & Experience
- 2+ years of experience in Machine Learning / AI engineering roles
- Hands-on experience with Generative AI / LLMs / Transformers
- Strong programming skills in Python and frameworks like PyTorch/TensorFlow
- Experience with vector databases (Pinecone, ChromaDB, Weaviate, FAISS)
- Solid understanding of LangChain, LlamaIndex, RAG, embeddings, prompt tuning
- Familiarity with cloud deployment (AWS/GCP/Azure) and CI/CD pipelines
- Strong analytical problem-solving skills and knowledge of ML lifecycle
- Excellent communication and teamwork abilities
