Head of Data - ML and AI Engineering at Klimb.io – Bangalore | Jobyssey

Responsibilities:

Engineering Leadership: Build, scale, and mentor the core Data Engineering, ML Engineering, MLOps, and AI Platform engineering organisations, establishing engineering best practices and operational rigour across all squads.
Enterprise Decisioning Platform: Design, operationalise, and scale a centralised decisioning platform integrating low-code model development, AutoML, real-time rule engines, policy-as-code, and automated workflow execution.
Data Platform Architecture: Direct the strategic vision and implementation of scalable cloud-native Lakehouse architectures (Databricks Delta Lake, Unity Catalogue, DLT, Spark) for high-volume batch and real-time streaming data ingestion.
MLOps and LLMOps Standardisation: Establish standardised production-grade MLOps frameworks and LLM/Agentic AI infrastructure
Production Deployment and Scaling: Partner closely with Data Science teams to ensure the seamless, automated transition of machine learning and deep learning models from experimentation into high-availability production environments.
Operational Excellence and SRE: Lead DataOps and SRE functions to guarantee platform uptime (> 99.5%), proactive automated testing, self-healing systems, and continuous CI/CD delivery pipelines.
Model and Data Governance: Operationalise comprehensive model lifecycle governance and regulatory compliance frameworks aligned to RBI FREE-AI guidelines, DPDP Act requirements, and internal risk policies.
Observability and System Health: Instrument end-to-end telemetry and monitoring across three layers: delivery health, platform health (SLI/SLO adherence for Tier-1 pipelines, MTTR), and model performance health (drift detection, feature freshness, retraining triggers).
FinOps and Cloud Optimisation: Drive FinOps discipline across multi-cloud and platform footprints (AWS, Databricks, vendor tools), publishing unit cost economics and managing cost optimisation at scale.
Data Security and Lineage: Enforce enterprise data governance constructs, including data lineage, access-control policies (Unity Catalogue RBAC/ABAC), PII masking, data cataloguing, and audit-ready technical documentation.
Decisioning System Expansion: Expand automated decisioning systems across core functional pods, including credit underwriting, risk assessment, loan pricing, collections, fraud detection, and customer management.
Cross-Functional and Executive Alignment: Act as the primary platform execution partner to the Head of Product & Strategy, Chief Risk Officer (CRO), and functional leadership to deliver on high-value business KPIs.
Incident and Reliability Governance: Own incident command and governance for critical data pipelines and AI platform services, running blameless postmortems, root-cause analysis, and corrective-action tracking.
Culture and AI-Native Innovation: Cultivate a high-performing engineering culture by recruiting and retaining top talent while championing AI-native development workflows (leveraging coding agents, LLMs, and automated tooling).

Requirements:

Overall Experience: 15-20 years of total experience in data engineering, software platform architecture, and machine learning infrastructure, with at least 8-10 years spent in senior management or platform leadership roles.
Top-Tier Education: Bachelor's, Master's, or PhD in Computer Science, Software Engineering, or a related quantitative field from a top-tier institution (IIT, IISc, BITS, NIT, or international equivalent).
Modern Data Stack Mastery: Deep architectural expertise in modern distributed data ecosystems, including Databricks (Delta Lake, Unity Catalogue, DLT, MLflow, Mosaic AI), Apache Spark, Kafka, Airflow, and Delta Lakehouse environments.
MLOps and AI Infrastructure: Proven track record of architecting and operating enterprise MLOps platforms, feature stores, model registries, evaluation harnesses, and LLM/Agentic AI infrastructure (RAG, LangGraph, CrewAI).
Decisioning Platform Expertise: Hands-on experience operationalizing centralized real-time decisioning platforms combining complex policy-as-code, business rule engines, and real-time scoring APIs.
Cloud and Infrastructure Architecture: Advanced proficiency in cloud-native platforms (AWS / GCP), container orchestration (Kubernetes, Docker), event-driven microservices, and Infrastructure-as-Code (Terraform, CloudFormation).
Programming and Engineering Rigour: Expert-level engineering background in Python, SQL, and Scala/Java, with strong adherence to design patterns, automated testing, and CI/CD pipelines.
Regulatory and Compliance Fluency: Solid experience operating within regulated financial environments, with direct exposure to RBI digital lending guidelines, RBI FREE-AI mandates, DPDP Act, and GDPR compliance.
Observability and SRE Practice: Strong technical background in establishing observability frameworks (SLI/SLO design, distributed tracing) using tools such as Datadog, Grafana, OpenTelemetry, or Databricks Lakehouse Monitoring.
FinOps and Cost Management: Demonstrated success in managing large-scale infrastructure budgets, optimising cloud compute utilisation, and driving FinOps principles across enterprise platform spend.
Organisational Leadership: Proven track record of building, mentoring, and managing multi-disciplinary engineering organisations of 20+ engineers and technical leads.
Fintech / Lending Domain Expertise: Substantial domain expertise within Fintech, NBFC, or retail banking environments, supporting credit risk, underwriting, collections, or fraud systems.
Delivery and Program Management: Expertise in Agile/Scrum execution at scale, OKR/KPI cascade design, RAID logs, dependency management, and technical documentation (ADRs, TDDs, Architecture Blueprints).
Executive and Vendor Stakeholder Presence: Exceptional written and verbal communication skills, with a demonstrated ability to synthesize technical trade-offs, align cross-functional partners, and negotiate with enterprise technology vendors.

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