Role & responsibilities

This is a hands on engineering manager. Need to spend 70% time coding (MUST HAVE) and 30% time leading. One way to filter is to look for candidates with positive / proven github codes published so that I can see their coding skills. candidates from currently an engineering manager or principal engineer with evidence they are writing codes on Azure for production systems. I am particularly looking for candidates who can help to use agentic ai to automate software development, CI CD to production.

- Required Qualifications

Education

- Masters or Phd degree in Computer Science, AI, Software Engineering, Data Science, Operational Research,

Engineering, or related fields.
- Advanced degree is preferred.

Experience

- Eight or more years of experience across software engineering, platform engineering, data engineering, analytics engineering, knowledge engineering, or AI engineering.
- Three or more years leading engineering teams, technical programs, or enterprise platform delivery.
- Demonstrated experience taking AI, analytics, automation, or data products from concept through production and support.
- Experience with enterprise BI platforms, semantic models, governed metrics, or analytics product development.
- Experience designing enterprise integrations, APIs, cloud-native solutions, and secure data access patterns.
- Experience leading cross-functional teams and managing technical delivery partners.

Leadership Competencies

- Enterprise builder with a product and platform mindset.
- Strong business acumen and focus on measurable value realization.
- Ability to simplify complex technical concepts for executives and business stakeholders.




- Strategic thinker who can also drive disciplined execution.
- Influential collaborator across organizational and functional boundaries.
- Pragmatic decision-maker who balances innovation, speed, risk, quality, and cost.
- Comfortable operating in ambiguity and shaping new capabilities.
- Commitment to engineering excellence, reuse, governance, and operational ownership.

Practice & Operating Model

Establish the Agentic & Intelligence Engineering practice, role clarity, delivery lifecycle, architecture standards, governance checkpoints, and production support model.

Semantic & KPI Foundation

Launch a governed KPI catalog and business glossary; define ownership,

certification, lineage, and change-management standards for priority enterprise metrics.

Ontology & Knowledge

Context & Reuse

Deliver shared context, retrieval, evaluation, observability, and integration components that accelerate multiple AI and BI initiatives.

Agentic BI

Launch governed AI-assisted analytics capabilities such as KPI explanation,

executive narratives, conversational analysis, or draft report generation for priority use cases.

Production Agents

Move multiple high-value assistants, agents, or intelligent workflows into stable production use with defined owners, controls, monitoring, and outcome measures.

Team & Partners

Build the initial team capability; establish partner engineering standards; improve delivery predictability, documentation, quality, and reuse.

Business Value

Demonstrate measurable improvement in time-to-value, decision quality,

productivity, operational execution, or avoided duplication across prioritized initiatives.

📌 Permanent Remote opportunity _ AI Manager (Delhi)
🏢 Aphthartos Technologies
📍 Delhi