AI/ML Engineer – Generative AI

Job Title: AI/ML Engineer – Generative AI

Location : PuneRole Summary

The AI/ML Engineer – Generative AI & RAG will be responsible for designing, developing, and deploying state-of-the-art machine learning models with a focus on Generative AI and Retrieval-Augmented Generation (RAG). The role involves building scalable solutions using Python, large language models (LLMs), and retrieval pipelines to solve complex business problems and enhance product capabilities.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

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