Website:
dignifyd.io
Job details:
AI/ML Engineer — Senior
Location: South Delhi, NCR | Employment Type: Full-time | Reports to: Head of Technology
Role Summary
The Senior AI/ML Engineer owns the architecture and production reliability of ML deployment pipeline, working in close partnership with Data Science on model handoff and with DevOps on infrastructure. This role also carries mentorship responsibility for the Junior AI/ML Engineer and effectively sets the technical bar for the AI/ML function.
Key Responsibilities
- Own end-to-end ML deployment architecture, including pipeline design and infrastructure decisions.
- Monitor and continuously optimize production model performance (accuracy, latency, drift).
- Partner with Data Science on model handoff criteria and validation before production release.
- Partner with DevOps on infrastructure provisioning and scaling for ML workloads.
- Set and enforce technical standards, including code review practices, for the AI/ML pod.
- Mentor the Junior AI/ML Engineer, including structured ramp-up and skill development.
- Evaluate and recommend new tools, frameworks, or practices to improve the ML pipeline.
Key Result Areas (KRAs)
- Model Deployment & MLOps — deploy models to production reliably and maintain ML pipelines.
- Production Performance — monitor and maintain model performance (accuracy, latency, drift) in production.
- Collaboration — partner with Data Science on model handoff and DevOps on infrastructure.
- Code Quality & Documentation — maintain clean, tested, documented ML code.
- Technical Mentorship — mentor the Junior AI/ML Engineer and set technical standards for the pod.
Required Skills & Technical Competencies
- Strong hands-on experience with ML deployment and MLOps tooling (e.g., MLflow, Kubeflow, or equivalent).
- Experience with cloud ML infrastructure (AWS/Azure/GCP) and containerization (Docker/Kubernetes).
- Strong software engineering fundamentals — testing, CI/CD, code review.
- Experience mentoring junior engineers.
Targeted Qualifications
- 5–8 years of experience in ML engineering / MLOps.
- Demonstrated track record deploying and maintaining ML models in production.
- Experience with cloud ML infrastructure (AWS/Azure/GCP).
- Bachelor's/Master's in Computer Science or related field.
Preferred / Nice-to-Have
- Experience with IoT/edge ML deployment.
- Contributions to open-source ML tooling or published technical work.
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