Role: Generative AI Expert / Applied AI Engineer

Experience

- 6–8 years of experience in AI / ML / software engineering

- 1–2 years of hands-on experience with LLMs, GenAI applications, prompt tuning, or fine tuning

- Prior experience building Deep Learning / Machine Learning systems in production environments

What This Person Will Own

- Upgrade and improve AI models based on user feedback, domain learnings, and business needs

- Fine-tune models or perform prompt tuning depending on the use case

- Improve AI outputs for accuracy, relevance, speed, scalability, explainability, and robustness

- Continuously evaluate latest LLMs, embeddings models, AI agents, and GenAI platforms

- Recommend and implement model upgrades where required

- Build feedback loops to improve model quality over time

- Test AI outputs from a user’s perspective and ensure responses are practical and trustworthy

- Create evaluation mechanisms for hallucination, accuracy, latency, cost, and quality

- Build and maintain MLOps pipelines for model testing, deployment, monitoring, versioning, and rollback

- Work with engineering teams to integrate AI models into production systems

- Apply procurement domain knowledge to all agentic workflows with a continuous feedback look

Must Have

- Robust understanding of machine learning, deep learning, NLP, and GenAI concepts

- Hands-on experience with LLMs, prompt engineering, prompt tuning, RAG, embeddings, and vector databases

- Experience with model fine-tuning, instruction tuning, or domain adaptation

- Knowledge of AI guardrails, responsible AI, explainability, transparency, safety, and security, and experience with hallucination detection, red-teaming, bias checks, or model evaluation frameworks

- Strong Python skills





- Experience with frameworks such as PyTorch, TensorFlow, Hugging Face, Sentence-Transformers, LangChain, LlamaIndex, or similar (Not all)

- Experience with at least one AI platform such as OpenAI, Azure AI, Gemini / Vertex AI, Claude, AWS Bedrock, or similar (Not all)

- Ability to evaluate model quality using user feedback, test datasets, and business scenarios

- Understanding of AI model deployment, monitoring, observability, and version control

- Product thinking and ability to work with Product, Engineering, Sales, Customer Success, and business teams

- Strong problem-solving mindset and ability to build practical AI solutions quickly

Good to Have

- Experience with procurement, sourcing, supply chain, contracts, vendor management, or enterprise workflows

- Experience with Docker, Kubernetes, CI/CD, MLflow, Weights & Biases, Airflow, or similar MLOps tools

- Basic understanding of new-age AI evaluation standards, metrics and international laws

- Experience working directly with customers or using customer feedback to improve AI systems

Role Expectations

- Take ownership of AI model performance and continuous improvement

- Convert user feedback and procurement-specific learnings into better prompts, models, and workflows

- Decide when to use prompt tuning, RAG, fine-tuning, model switching, or workflow changes

- Keep the AI system updated with newer and better models

- Ensure AI outputs are accurate, explainable, reliable, and useful for end users

- Build quick prototypes, test them, and move successful ideas into production

- Work closely with engineering to make AI systems scalable, monitored, and production-ready

- Learn the procurement domain over time and apply that understanding to improve AI quality

📌 Gen AI Expert (Bengaluru)
🏢 Aerchain
📍 Bengaluru