AI ML Engineer

October 31

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SyncIQ.ai

WebsiteLinkedInAll Job Openings

Enterprise Software • Advanced AI Application • Workforce Efficiency • Generative AI • Large Language Models

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

Description

• About SyncIQ.ai: SyncIQ.ai is at the forefront of transforming business processes through advanced AI and automation. • Job Description: We are seeking a highly skilled and innovative AI Research Scientist specializing in Generative AI. • Key Responsibilities: Stay up-to-date with the latest research in Generative AI, NLP, and related fields... • Implement Advanced GenAI Techniques: Apply cutting-edge GenAI research findings to develop innovative solutions... • Prototype Development: Build prototypes to test and validate new concepts... • Fine-Tuning Models: Fine-tune pre-trained models when necessary... • Task Runner Development: Develop and enhance task runners... • Collaboration with Engineering/MLOps Teams: Work closely with engineering and MLOps teams... • Problem Solving: Translate complex business problems into well-defined AI solutions... • Documentation and Communication: Document methodologies and communicate findings...

Requirements

• Education: Master's or Ph.D. in Machine Learning, Natural Language Processing, or related fields. • Experience: 5-6 years in AI research, NLP, Machine Learning, or related fields. • GenAI Expertise: Deep understanding of NLP AI models and techniques, including experience with models like GPT, T5, BART, etc. • Research Proficiency: Proven ability to read, understand, and implement findings from the latest AI research papers. • Technical Skills: Proficiency with AI/ML frameworks such as PyTorch, TensorFlow, and libraries like Hugging Face Transformers. • Problem-Solving Abilities: Strong skills in conceptualizing and implementing AI solutions to complex problems. • Collaboration Skills: Excellent communication skills with the ability to work effectively in a team environment and collaborate with cross-functional teams. • Deployment Knowledge: Familiarity with working alongside engineering and MLOps teams to deploy models in production environments. • Preferred Qualifications: • Advanced Research Implementation: Experience implementing novel research findings in practical applications. • RAG and Structured Outputs: Familiarity with new types of Retrieval-Augmented Generation and generating structured outputs from GenAI models. • Specialized Models: Experience with domain-specific models or fine-tuning techniques. • Cloud and Big Data: Understanding of cloud-based AI tools and large-scale data processing (deployment skills are a plus but not required). • Vector Databases: Experience with vector databases (e.g., FAISS, Pinecone) to enhance information retrieval in NLP applications. • Multi-Lingual Models: Experience working with multi-lingual models or zero-shot learning techniques.

Benefits

• Innovative Projects: Opportunity to work on groundbreaking GenAI solutions with real-world business applications. • Collaborative Environment: A supportive and innovative work culture that encourages creativity and problem-solving. • Flexible Work Options: Remote work flexibility with the potential for a hybrid setup in the future. • Professional Growth: Opportunities for continuous learning and professional development.