Machine Learning Engineer

Description

Role Overview:

We are looking for an innovative and highly engaged Machine Learning Engineer to join our global team. You will be responsible for designing and maintaining scalable ML pipelines on AWS, optimizing vector databases for retrieval-augmented generation (RAG), and ensuring our Generative AI chatbot delivers state-of-the-art performance. You should be eager to experiment with new ideas, collaborate with a diverse international team, and help build the next generation of AI-powered SaaS solutions.

Key Responsibilities:

Generative AI & Model Optimization

• Train, fine-tune, and optimize LLM-powered applications, including chatbots, NER models, and document intelligence solutions.
• Enhance retrieval-augmented generation (RAG) pipelines by integrating memory-aware, multi-modal retrieval techniques.
• Experiment with model quantization, distillation, and fine-tuning strategies to improve efficiency and inference speed.

ML Infrastructure & Data Engineering

• Design, implement, and maintain scalable, cloud-native ML pipelines on AWS for real-time and batch processing.
• Optimize vector database architectures (e.g., Weaviate, Pinecone, FAISS) to enhance chatbot response accuracy.
• Develop and deploy large-scale data pipelines for data ingestion, transformation, and processing of enterprise-scale datasets.
• Ensure system scalability, reliability, and cost efficiency in serverless and containerized cloud environments (AWS Lambda, ECS).

Innovation & Best Practices:

• Drive continuous improvement by identifying opportunities for optimizing the performance, scalability, and cost-effectiveness of our ML-based solutions.
• Stay informed about emerging trends in AI and ML, attending technical seminars, workshops, and industry events to ensure our solutions remain competitive.

Collaboration & Innovation:

• Work closely with Data Scientists, MLOps Engineers, and Software Developers to integrate AI solutions into the SaaS platform.
• Collaborate with the Product Team to translate business needs into ML-driven features that improve user experience.
• Drive a culture of experimentationtest new ML frameworks, embeddings, and cloud optimizations.

Qualifications:

• Strong experience in AI/ML development, particularly in Generative AI, NER, and chatbot applications.
• Expertise in machine learning frameworks like PyTorch, TensorFlow, and DSPy.
• Proficiency with MLOps tools such as MLFlow, and Docker for containerization.
• Familiarity with cloud platforms such as AWS, including deploying and managing ML workloads in cloud-native environments.
• Solid understanding of vector databases and experience with optimizing and scaling large datasets.
• Experience working in Agile/Scrum environments
• Ability to work in a collaborative, fast-paced global team spread across multiple time zones.
• Excellent communication skills, both for collaborating with non-technical stakeholders and producing high-quality technical documentation.

Preferred Qualifications:

• Prior experience in LLM fine-tuning, prompt engineering, or RAG-based architectures.
• Familiarity with MLOps tools such as Docker, Kubernetes, MLFlow, and Airflow.
• published research papers, blogs, or open-source contributions in AI/ML topics.