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Overview
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 solutioning—from 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 strong 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
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Hiring Related Queries
India:
HiringsupportIndia@fractal.ai
Outside India:
HiringsupportROW@fractal.ai
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