AI/ML Engineer – Generative AI

Job Title:

Location :

Key Responsibilities

• Design, develop, and deploy scalable ML solutions using LLMs and RAG architecture.
• Implement and optimize prompt engineering, embeddings, vector databases, and retrieval mechanisms.
• Develop APIs and services to integrate AI solutions into production systems.
• Collaborate with data engineers, product managers, and other stakeholders to define technical requirements.
• Continuously evaluate and improve model performance and accuracy.
• Stay updated with the latest research and developments in GenAI, NLP, and ML.
• Contribute to technical documentation, code reviews, and best practices.
• Ensure all AI solutions comply with ethical and data privacy standards.

Required Qualifications & Skills

• Bachelor's or Master’s degree in Computer Science, AI, Data Science, or related field.
• Strong programming skills in

  Python

  , including use of libraries like TensorFlow, PyTorch, Hugging Face Transformers, LangChain, etc.
• Experience with

  LLMs

  (GPT, LLaMA, Claude, etc.) and

  RAG

  frameworks.
• Hands-on experience with vector databases (e.g., FAISS, Pinecone, Weaviate).
• Solid understanding of NLP, embeddings, tokenization, and attention mechanisms.
• Familiarity with APIs, RESTful services, and cloud deployment (AWS/GCP/Azure).
• Strong foundation in data structures, algorithms, and ML workflows.

Soft Skills

• Strong problem-solving and analytical thinking.
• Excellent verbal and written communication skills.
• Adaptability and willingness to learn emerging technologies.
• Collaboration and teamwork in cross-functional environments.
• Strong attention to detail and ability to deliver high-quality work.

Preferred Qualifications

• Ph.D. in a relevant field.
• Experience deploying LLMs in production environments.
• Contributions to open-source GenAI projects or research publications.
• Knowledge of MLOps and CI/CD for ML pipelines.
• Experience with Kubernetes, Docker, or other containerization tools.

Key Relationships

• AI/ML Team
• Data Engineering Team
• Product Management
• Software Development
• IT Security & Compliance
• Business Stakeholders

Role Dimensions

• Team Size Managed:

  None / Optional (if leadership role)

• Budget Responsibility:

  None / Optional

• Scope of Impact:

  Company-wide AI capabilities and customer-facing features

• Decision-Making Authority:

  Technical implementation within AI projects

Success Measures (KPIs)

• Time to deploy new ML/GenAI features
• Accuracy and performance of deployed models
• User engagement and satisfaction with AI features
• Uptime and reliability of AI services
• Contribution to AI/ML research or innovation within the team
• Reusability and scalability of AI code and systems

Competency Framework Alignment

• Technical Expertise:

  Mastery in Python, ML frameworks, GenAI, and RAG

• Innovation:

  Drives AI innovation and creative problem-solving

• Collaboration:

  Works effectively in multi-disciplinary teams

• Execution Excellence:

  Delivers high-quality, scalable AI solutions

• Customer Focus:

  Builds AI features that enhance user experience

• Ethical Responsibility:

  Ensures responsible AI practices and data governance

Thanks,

Renuka

renuka.thakur@eminds.ai