
Senior Machine Learning Engineer
4 days ago
We are seeking a skilled Machine Learning Engineer to join our team. The ideal candidate will be responsible for designing, developing and deploying state-of-the-art machine learning models with a focus on Generative AI.
This role involves building scalable solutions using Python, large language models (LLMs) and retrieval pipelines to solve complex business problems and enhance product capabilities.
Key Responsibilities:- Design, develop and deploy ML solutions using LLMs and RAG architecture.
- Implement and optimize prompt engineering, embeddings, vector databases and retrieval mechanisms.
- Develop APIs and services to integrate AI solutions into production systems.
- Collaborate with data engineers, product managers and other stakeholders to define technical requirements.
- Continuously evaluate and improve model performance and accuracy.
- Stay updated with the latest research and developments in GenAI, NLP and ML.
- Contribute to technical documentation, code reviews and best practices.
- Ensure all AI solutions comply with ethical and data privacy standards.
- Bachelor's or Master's degree in Computer Science, AI, Data Science or related field.
- Strong programming skills in Python including use of libraries like TensorFlow, PyTorch, Hugging Face Transformers, LangChain etc.
- Experience with LLMs (GPT, LLaMA, Claude etc.) and RAG frameworks.
- Hands-on experience with vector databases (e.g. FAISS, Pinecone, Weaviate).
- Solid understanding of NLP, embeddings, tokenization and attention mechanisms.
- Familiarity with APIs, RESTful services and cloud deployment (AWS/GCP/Azure).
- Strong foundation in data structures, algorithms and ML workflows.
- Ph.D. in a relevant field.
- Experience deploying LLMs in production environments.
- Contributions to open-source GenAI projects or research publications.
- Knowledge of MLOps and CI/CD for ML pipelines.
- Experience with Kubernetes, Docker or other containerization tools.
- Team Size Managed: None / Optional (if leadership role)
- Scope of Impact: Company-wide AI capabilities and customer-facing features
- Decision-Making Authority: Technical implementation within AI projects
- Time to deploy new ML/GenAI features
- Accuracy and performance of deployed models
- User engagement and satisfaction with AI features
- Uptime and reliability of AI services
- Contribution to AI/ML research or innovation within the team
- Reusability and scalability of AI code and systems
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