Eminds.ai - Generative AI/Machine Learning Engineer - LLM

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Role Summary

Key Responsibilities

• Design, develop, and deploy AI/ML models and Generative AI applications for diverse enterprise use cases.
• Implement, fine-tune, and integrate Large Language Models (LLMs) using frameworks like LangChain, LlamaIndex, and RAG pipelines.
• Build Agentic AI systems with multi-step reasoning and autonomous decision-making capabilities.
• Create secure and scalable data ingestion pipelines for structured and unstructured data, enabling indexing, vector search, and advanced retrieval.
• Collaborate with cross-functional teams (Data Engineers, Product Managers, Architects) to operationalize AI solutions.
• Build CI/CD pipelines for ML/GenAI workflows and support end-to-end MLOps practices.
• Leverage Azure and Databricks for training, serving, and monitoring AI models at scale.
  

Required Qualifications & Skills (Mandatory)

• 4+ years of hands-on experience in AI/ML development, including Generative AI applications.
• Expertise in RAG, LLMs, and Agentic AI implementations.
• Strong knowledge of LangChain, LlamaIndex, or similar LLM orchestration frameworks.
• Proficient in Python and key ML/DL libraries : TensorFlow, PyTorch, Scikit-learn.
• Solid foundation in Deep Learning, Natural Language Processing (NLP), and Transformer-based architectures.
• Experience in building data ingestion, indexing, and retrieval pipelines for real-world enterprise scenarios.
• Hands-on experience with Azure cloud services and Databricks.
• Proven experience designing CI/CD pipelines and working with MLOps tools like MLflow, DVC, or Kubeflow.
  

Soft Skills

• Strong problem-solving and critical thinking ability.
• Excellent communication skills, with the ability to explain complex AI concepts to non-technical stakeholders.
• Strong collaboration and teamwork in agile, cross-functional environments.
• Growth mindset with curiosity to explore and learn emerging technologies.
  

Preferred Qualifications

• Familiarity with vector databases : FAISS, Pinecone, Weaviate.
• Experience with AutoGPT, CrewAI, or similar agent frameworks.
• Exposure to Azure OpenAI, Cognitive Search, or Databricks ML tools.
• Understanding of AI security, responsible AI, and model governance.
  

Key Relationships

• Internal : Data Scientists, Data Engineers, DevOps Engineers, Product Managers, Solution Architects.
• External : AI/ML platform vendors, cloud service providers (Microsoft Azure), third-party data providers.
  

Role Dimensions

• Contribute to AI strategy, architecture, and reusable AI components.
• Support multiple projects simultaneously in a fast-paced agile environment.
• Mentor junior engineers and contribute to best practices and standards.
  

Success Measures (KPIs)

• % reduction in model development time using reusable pipelines.
• Successful deployment of GenAI/LLM features in production.
• Accuracy, latency, and relevance improvements in AI search and retrieval.
• Uptime and scalability of deployed AI models.
• Integration of responsible AI and compliance practices.
  

Competency Framework Alignment

• Technical Excellence in AI/ML/GenAI
• Cloud Engineering & DevOps Enablement
• Innovation & Continuous Improvement
• Business Value Orientation
• Agile Execution & Ownership
• Cross-functional Collaboration