Dedicatted

We are seeking a skilled Machine Learning Engineer with a focus on Generative AI and Large Language Models (LLMs) to join our team.


Responsibilities: 
 

• Prompt Engineering: Design, test, and optimize prompts for LLMs to enhance user interaction and generate contextually relevant results.
• Agent-Based Systems & RAG: Develop intelligent agent systems and implement Retrieval-Augmented Generation (RAG) pipelines for applications such as task automation and dynamic content generation.
• Data Pipeline Development: Build and maintain reliable data pipelines to support LLM training and inference workflows, ensuring seamless integration with other systems.
• Generative AI Development: Develop, train, fine-tune, and evaluate large language models (LLMs) and generative AI models for diverse business use cases.
• Performance Optimization: Conduct rigorous evaluations and performance tuning for LLMs to meet production-grade requirements for latency, accuracy, and robustness.
• Security & Compliance: Apply best practices for data security and compliance, especially when handling sensitive or regulated data.
• Cross-Functional Collaboration: Work closely with product, engineering, and data science teams to align AI models with product requirements and business objectives. 

Experience: 
 

• 2-3 years of experience in Machine Learning with a focus on Generative AI and LLMs. 
• Experience with AWS Bedrock and related services for large model deployment.
• Proven experience with self-managed ML environments and infrastructure (e.g., Kubernetes, Docker, Serverless).

Technical Skills: 
 

• Proficiency in Python, including experience with AWS Lambda.
• Knowledge of Generative AI techniques, including Zero-Shot, Few-Shot, and Chain-of-Thought prompting.
• Demonstrated ability in LLM training, fine-tuning, and evaluation.
• Familiarity with MLOps tools and practices, including versioning, monitoring, and CI/CD pipelines.

Would be a plus:
 

• Experience with TensorFlow, and/or PyTorch
• Familiarity with MLOps tools like MLflow, Kubeflow, or SageMaker Pipelines for versioning, monitoring, and CI/CD integration.

Working conditions:
 

• Opportunity to work with a diverse portfolio of clients and industries, providing unique challenges and opportunities for growth.
• Collaborative and supportive work environment with a focus on continuous learning and professional development.
• Paid Vacation (16 days), Documented/Undocumented Sick Leave, Leave for Significant Life Events;
• Quarterly Sports/English bonuses;
• Compensation for medical insurance;
• Flexible working hours and remote work options available;
• Exciting events, corporate parties, and pizza parties at the office;
• Gifts from the company;
• Friendly environment and a wonderful team.

Join us!