Senior Machine Learning Engineer

Key Responsibilities

• Design, develop, fine-tune, and deploy ML/AI models, especially LLMs (Large Language Models), SLMs (Small Language Models), and VLLMs.
• Work on advanced RAG architectures, including Knowledge-based RAG and Graph-based RAG techniques.
• Engineer high-quality prompts and chain-of-thought logic for agentic AI systems.
• Implement model optimization strategies such as fine-tuning, pruning, and distillation.
• Integrate vector databases (e.g., FAISS, Pinecone, Weaviate) and graph databases (e.g., Neo4j, TigerGraph) into scalable AI pipelines.
• Collaborate with data scientists to transform ML prototypes into production-grade software applications.
• Design and maintain APIs and ML microservices for real-time and batch inference.
• Ensure model performance, reliability, and security in production environments.
• Contribute to the development of AI-driven intelligent agents and decision-making systems.

Required Skills & Experience

• 4–7 years of experience in Machine Learning, with a strong focus on LLMs and applied NLP.
• Hands-on experience with:
• VLLMs, LLMs, SLMs
• Knowledge-based RAG, Graph-based RAG
• Prompt Engineering and Prompt Optimization
• Model Fine-tuning and Distillation techniques
• Vector Databases (e.g., FAISS, Pinecone)
• Graph Databases (e.g., Neo4j, TigerGraph)
• Agentic AI design and development
• Strong programming skills in Python; experience with libraries such as Hugging Face Transformers, LangChain, PyTorch, or TensorFlow.
• Familiarity with MLOps tools and frameworks for deployment and monitoring.
• Experience integrating AI models into production-grade software systems.
• Excellent problem-solving, communication, and collaboration skills.

Preferred Qualifications

• Master’s or Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field.
• Experience working in client-facing roles or international projects.
• Understanding of cloud platforms (AWS, GCP, or Azure) for ML model deployment.