Job SummaryAI/ML Technical Lead Predictive Observability & Reliability Engineering

Role Title: AI/ML Technical Lead
Experience: 5-8 years in AI/ML engineering, with 2-3 years in a technical lead/mentoring capacity
Location: Bangalore
Employment Type: Full time

We are looking for a hands-on AI/ML Technical Lead to drive model development, mentor engineers, and deliver production-grade AI/ML solutions for predictive observability and reliability use cases. The role bridges platform architecture with day-to-day technical execution and team delivery.

Key Responsibilities

- Lead the design, development, and deployment of ML/DL models for anomaly detection, predictive failure analysis, and Root Cause Analysis (RCA).
- Own the end-to-end ML pipeline: data ingestion, feature engineering (rolling windows, lag features, aggregations), model training, scoring, and RCA generation.
- Build and tune unsupervised models (Isolation Forest, Autoencoders) and supervised/time-series models (LSTM) for degradation prediction and risk scoring.
- Translate business/operational requirements into technical model specifications and success metrics (MTTD, MTTR, MTBF improvements).
- Guide and mentor engineers, including upskilling them in Python, data engineering, and model development practices.
- Collaborate with architecture stakeholders to ensure models integrate cleanly into the broader observability/data platform.




- Drive technical delivery across phased rollouts - from POC/MVP to validation to full production experience - including dashboards, alerting, and explanation summaries.
- Establish coding standards, model validation practices, and documentation for model architecture and deployment strategy.

Required Skill Set

- 5-8 years in AI/ML engineering, with 2-3 years in a technical lead or mentoring capacity.
- Strong hands-on Python skills; proven experience with scikit-learn, TensorFlow/PyTorch.
- Practical expertise in unsupervised learning (clustering, Isolation Forest, Autoencoders) and deep learning for time-series (LSTM/GRU).
- Experience building feature engineering pipelines from raw telemetry/log data (Azure Log Analytics, Databricks, or similar).
- Solid understanding of observability concepts - anomaly detection, event correlation, RCA, SLIs/SLOs.
- Experience leading or mentoring small technical teams, including reviewing code and guiding model development decisions.
- Strong communication skills to bridge between operations stakeholders and technical implementation.

Preferred Qualifications

- Experience with real-time streaming pipelines and automated alerting/remediation systems.
- Familiarity with Microsoft Fabric, Azure Databricks, or unified data platforms.
- Exposure to Copilot Studio or agentic AI frameworks for conversational operational insights.
- Prior experience upskilling engineers or ops teams into AI/ML-capable roles.

Key Responsibilities

- Same as above

Skill Requirements

- Same as above

Other Requirements

- Same as above

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