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

Solid 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