Responsibilities
- Work on the end-to-end design and deployment of scalable AI and machine learning solutions in production environments.
- Develop and optimize large language model (LLM) applications using frameworks such as LangChain, LlamaIndex, and Haystack.
- Drive Retrieval-Augmented Generation (RAG) pipelines with vector databases like Pinecone, FAISS, Weaviate, and Milvus.
- Collaborate closely with other data scientists to transition models smoothly from research to production.
- Build and manage comprehensive MLOps pipelines encompassing training, testing, deployment, monitoring, and retraining.
- Architect AI solutions integrated into enterprise applications via APIs and microservices.
- Stay updated on AI and Generative AI research, continually assessing emerging frameworks for adoption.
- Mentor junior AI engineers and promote a culture of best practices in AI engineering.
- Work with business stakeholders to translate requirements into AI-driven outcomes.
- Ensure adherence to responsible AI principles, including bias mitigation, fairness, and explainability.
Requirements
- 5–8 years of experience in AI/ML engineering, with at least 2 years in leading AI initiatives.
- Strong expertise in Python, TensorFlow, PyTorch, Hugging Face Transformers.
- Hands-on experience with LangChain or similar LLM application frameworks.
- Solid understanding of vector databases, RAG architectures, and prompt engineering.
- Proficiency in MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
- Familiarity with cloud AI platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
- Strong background in data pipelines, APIs, and scalable system design.
Nice to Have
- Experience fine-tuning LLMs (LoRA, PEFT, parameter-efficient training).
- Knowledge of computer vision or multimodal AI.
- Familiarity with responsible AI frameworks and explainability tools (SHAP, LIME, Captum).
- Contributions to open-source AI projects.
