We are looking for a Head of Engineering to lead our AI capability, spanning AI platforms, machine learning systems, and emerging agentic AI architectures. This role is responsible for driving end-to-end solutioningfrom client discussions and problem framing to execution strategy and production delivery.

You will operate at the intersection of client advisory, technical architecture, and execution leadership, building scalable foundations that power advanced AI and autonomous systems.

Key Responsibilities

1. Client Advisory & Problem Framing

- Lead senior-level client conversations to identify opportunities across data, AI, and agentic workflows
- Translate ambiguous business problems into structured AI/data solution blueprints
- Act as a trusted advisor on AI transformation roadmaps, including platform and operating model design
- Present architectures, trade-offs, and execution strategies to technical and non-technical stakeholders

2. Data & AI Foundations

- Define and drive architecture for modern data platforms (data lakes, lakehouses, real-time pipelines)
- Build scalable ML and AI foundations including feature stores, training pipelines, and inference systems
- Establish reusable frameworks, accelerators, and reference architectures
- Ensure solid governance across data quality, lineage, security, and compliance

3. Agentic AI Systems

- Lead design and implementation of agentic AI systems (LLM-based agents, RAG pipelines, multi-agent orchestration)
- Define patterns for tool use, memory, reasoning workflows, and human-in-the-loop systems
- Evaluate and integrate emerging GenAI technologies into production-grade solutions
- Drive experimentation industrialization of agentic use cases

4. Execution Strategy & Delivery Leadership

- Own end-to-end delivery:



discovery PoC MVP scaled production
- Build and lead cross-functional teams across data engineering, ML engineering, and AI engineering
- Define delivery plans, milestones, and measurable outcomes
- Manage risks, dependencies, and client expectations in complex engagements

5. Engineering Excellence & MLOps

- Establish best practices across CI/CD, MLOps, LLMOps, and observability
- Ensure robustness in model performance, monitoring, retraining, and lifecycle management
- Drive standards for reproducibility, evaluation, and system reliability

6. Capability Building & Influence

- Build internal capability across AI foundations and agentic systems
- Mentor engineering leaders and shape technical culture
- Contribute to thought leadership, GTM assets, and reusable IP
- Influence senior stakeholders across client and internal ecosystems

Qualifications

- 15 to 20 years in engineering across data, platforms, and AI/ML systems
- 5+ years in leadership roles managing large, distributed engineering teams
- Strong experience in data platforms (batch + streaming), cloud ecosystems (AWS/Azure/GCP)
- Deep expertise in ML systems, MLOps, and production AI architectures
- Hands-on experience with Generative AI and LLM-based systems (RAG, agents, orchestration frameworks)
- Proven ability to lead client-facing engagements and drive delivery from concept to production
- Excellent communication and executive stakeholder management skills

Preferred Qualifications

- Experience in consulting / AI services organizations
- Exposure to building agentic workflows in enterprise settings
- Familiarity with tools like LangChain, LlamaIndex, vector databases, orchestration frameworks
- Experience across multiple industries and large-scale transformations

📌 Lead AI Engineer- LLMOps/MLOps (Bengaluru)
🏢 Fractal Analytics
📍 Bengaluru