Hyderabad, Telangana
Job Summary

Looking for SDM candidate with below mentioned JD /skills.

12–16+ years of proven experience owning end-to-end technology delivery outcomes in complex engineering or transformation roles.
Demonstrated track record of direct accountability for delivery results, working closely and decisively with engineering and architecture teams.
Experience leading cross-functional delivery in matrixed organizations and managing third-party vendor performance to meet delivery commitments.
Relevant domain expertise preferred; experience in regulated industries such as insurance or finance is an advantage.
Strong, demonstrable experience in AI and automation initiatives, including designing or delivering AI/ML-enabled features, integrating ML models into production, or leading intelligent automation programs.
Degree in Computer Science, Engineering, or equivalent technical background.
Certifications in delivery/program management or architecture are beneficial but not mandatory.

Key Responsibilities

Execution & Delivery Leadership
Proven ability to own and drive delivery outcomes end to end in complex, cross-functional environments.
Expertise in delivery governance, RAID management, program cadence, and execution frameworks with a focus on decisive intervention and resolution.
Relentless focus on removing impediments and driving accountability rather than passive visibility or reporting.

Technical depth & authority
Strong technical expertise in AI/ML concepts with hands-on experience in model development, integration, and productionization.
Deep understanding of data platforms and analytics stacks underpinning AI/ML solutions, including data ingestion, ETL/streaming, warehousing, feature engineering, and serving.
Familiarity with ML lifecycle management, MLOps patterns, and relevant tooling.
Credibility and authority to challenge and influence engineering and architecture decisions related to AI/ML components, data pipelines, feature stores,



embedding/vector stores, and model serving infrastructure.
Practical, multi-layered approach to identifying and mitigating AI-driven risks such as hallucinations, inadequate guardrails, and data security concerns.
Solid grasp of integrations, system architecture, security, and non-functional requirements.
Ability to interrogate technical designs, quantify implementation risks, and communicate these effectively to business stakeholders.
Risk & escalation management
Proven capability to anticipate issues early, lead cross-team resolution efforts, and manage critical escalations with direct intervention.
Comfortable making pragmatic trade-offs under pressure to protect delivery outcomes.
Stakeholder influence & communication
Strong presence and influence with senior stakeholders, able to lead without direct authority.
Skilled at translating complex technical risks into transparent business impacts and presenting actionable choices.
Business domain understanding
Sufficient domain knowledge to ensure delivery relevance and prioritize work based on business impact, while respecting business ownership boundaries

Skill Requirements

Core Skills

Execution & Delivery Leadership
Proven ability to own and drive delivery outcomes end to end in complex, cross-functional environments.
Expertise in delivery governance, RAID management, program cadence, and execution frameworks with a focus on decisive intervention and resolution.
Relentless focus on removing impediments and driving accountability rather than passive visibility or reporting.

Technical depth & authority




Strong technical expertise in AI/ML concepts with hands-on experience in model development, integration, and productionization.
Deep understanding of data platforms and analytics stacks underpinning AI/ML solutions, including data ingestion, ETL/streaming, warehousing, feature engineering, and serving.
Familiarity with ML lifecycle management, MLOps patterns, and relevant tooling.
Credibility and authority to challenge and influence engineering and architecture decisions related to AI/ML components, data pipelines, feature stores, embedding/vector stores, and model serving infrastructure.
Practical, multi-layered approach to identifying and mitigating AI-driven risks such as hallucinations, inadequate guardrails, and data security concerns.
Solid grasp of integrations, system architecture, security, and non-functional requirements.
Ability to interrogate technical designs, quantify implementation risks, and communicate these effectively to business stakeholders.
Risk & escalation management
Proven capability to anticipate issues early, lead cross-team resolution efforts, and manage critical escalations with direct intervention.
Comfortable making pragmatic trade-offs under pressure to protect delivery outcomes.
Stakeholder influence & communication
Strong presence and influence with senior stakeholders, able to lead without direct authority.
Skilled at translating complex technical risks into clear business impacts and presenting actionable choices.
Business domain understanding
Sufficient domain knowledge to ensure delivery relevance and prioritize work based on business impact, while respecting business ownership boundaries

Other Requirements
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