The AI Engineer designs, builds, and operationalises machine learning and generative AI systems, taking them from
prototype to production. The role covers model development, LLM and agentic application engineering, and the data
and deployment pipelines that keep these systems accurate, scalable, and reliable.
Key Responsibilities:
• Build, evaluate, and deploy machine learning and deep learning models for production.
• Develop LLM-based and agentic applications, including RAG pipelines and tool use.
• Fine-tune and optimise models for accuracy, latency, and cost using PEFT and quantisation.
• Design evaluation frameworks, benchmarks, and guardrails to safeguard model quality.
• Build and automate data pipelines for training, inference, and model monitoring.
• Package and serve models as scalable APIs with containerisation, CI/CD, and MLOps.
• Stay updated with advancements in AI/ML and apply them to practical use cases.
Requirements
Educational Qualifications:
• B.E./B.Tech, M.Tech, B.Sc., M.Sc., MCA, or an equivalent degree in Computer Science, Information Technology,
Engineering, Mathematics, Statistics, or a related quantitative
field.
• Relevant certifications in cloud, AI/ML, or software
engineering are a plus.
Skillset:
• Strong programming skills in Python.
• Experience with ML/DL frameworks (e.g., scikit-learn, PyTorch,
or TensorFlow).
• Hands-on experience with LLM APIs, prompt engineering, and
agent frameworks (e.g., LangChain, LangGraph, MCP).
• Working knowledge of RAG, embeddings, and vector
databases (e.g., pgvector, FAISS, or Pinecone).
• Experience serving models as APIs (FastAPI, Docker) with
MLOps practices (MLflow, model versioning, monitoring).
• Proficiency with SQL and data pipeline tooling for large
datasets.
• Experience with at least one cloud platform (AWS, Azure, or
GCP).
Soft Skills:
• Adaptability in a fast-evolving technology landscape.
• Strong problem-solving ability with a structured, hands-on
approach.
• Critical thinking to evaluate trade-offs and validate AI
generated outputs.
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