Associate Data Scientist, AI Engineering
Associate Data Scientist, AI Engineering
trinity life sciencesFresher
- Posted 9 days ago
- Over 50 applicants have applied
Job Description
We're committed to bringing passion and customer focus to the business.
Key Responsibilities
Key Responsibilities
- Design and implement ML models including regression, classification, clustering, and optimization algorithms.
- Build and deploy GenAI applications using LLMs for tasks such as summarization, sentiment analysis, and content generation.
- Collaborate with cross-functional teams to identify opportunities for AI integration into business processes.
- Conduct research on emerging GenAI technologies and frameworks (e.g., LangChain, LlamaIndex).
- Fine-tune and optimize models for performance, scalability, and accuracy.
- Develop and maintain robust data pipelines and workflows.
- Stay updated with the latest advancements in AI, ML, and NLP.
More Info
Key Skills
LangChain
data pipelines
LLMs
ML models
GenAI applications
LlamaIndex
About Company
Skills:
Product Lifecycle Management (PLM), Traditional AI, Generative AI, Agentic AI, AI-enabled productivity solutions, Engineering product development processes, Documentation standards, Knowledge Management, Digital engineering automation, Engineering frameworks, Modular architectures, Design reuse strategies, Engineering operating models
Skills:
automation, Scripting, AI tools, programming fundamentals
Skills:
Devops, Data Science, Software Engineering, Azure, Python, Kubernetes, AWS, LangChain, Vector DB, Azure OpenAI, CI CD, Gen AI tools
Skills:
Architecture - ALL, Generative AI - Basic, Agile Testing - ALL, Enterprise Architecture, Agile Testing, MLops, chains, Conversational AI Platform, Model Optimization, Generative AI for Data Analytics, AI-AI Engineering, Technology, Digital Architecture, Architecture, LLMOps, AI-Generative AI, Databricks AI Engineering Services, AI/ML Solution Architecture and Design, Model Support, Prompt Engineering
Skills:
software engineering practices , Design Patterns, custom evaluation frameworks, in-context learning, compliance practices, model fine-tuning, backtesting, Process Optimization, AI technologies, decision automation, drift detection, Fraud Detection, Large Language Models, Governance, model monitoring