Senior AI/ML Engineer

Company Description

Aziro

Role Description

This is a full-time hybrid role for a Senior AI/ML Engineer based in Chennai/Bangalore/Pune, with some work-from-home flexibility.

Senior Artificial Intelligence & Machine Learning (AI/ML) Engineer

This role focuses on individual technical contribution and requires close collaboration with solution architects, AIML leads, and fellow engineers to translate business use cases into scalable, secure, cloud-native AI services.

The ideal candidate will bring deep technical expertise across the AI/ML lifecycle—from prototyping to deployment—while contributing to a culture of engineering excellence through peer reviews, documentation, and platform innovation. They will play a critical role in delivering robust, high-performance AI systems in partnership with the broader AI/ML team.  

Qualifications

• 6–10 years

  total software/ML engineering experience, including

  3+ years

  delivering ML models or GenAI systems to production.
• Proven track record building and optimising

  RAG or LLM‑powered

  applications at scale
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow) and in cloud‑native deployment (AWS/GCP/Azure).
• Hands‑on experience with vector databases, embeddings, and prompt engineering.
• Experience in regulated industries (Fintech, Healthcare, eCommerce) is a plus.
• Experience with

  multi‑agent

  frameworks (Crew AI, AutoGen, LangGraph).
• Certifications such as AWS Certified Machine Learning Specialty / Azure AI Engineer / Google Professional Machine Learning Engineer.
• Bachelor’s degree in Computer Science, Data Science, Engineering or related discipline (Master’s preferred).

• Ownership mindset:

  drives features from design through deployment and monitoring.

• Clear communicator:

  explains technical trade‑offs to stakeholders, writes concise docs, and updates project artefacts.

• Collaboration & mentorship:

  pairs with junior ngineers, shares knowledge in brown‑bag sessions, gives constructive PR feedback.

• Continuous learning:

  tracks latest GenAI research, evaluates new tooling, and proposes incremental improvements.