Head of AI Systems Engineering
Weekday
full-time
Posted on:
Location Type: Remote
Location: India
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Job Level
Tech Stack
About the role
- Lead the conversion of AI research outputs into stable, scalable, and production-ready systems
- Own the full lifecycle of deployed models, from initial validation to sunset and replacement
- Define clear standards for model readiness, performance thresholds, and operational handoff
- Ensure production AI systems meet reliability, latency, cost, and scalability expectations
- Architect and operate AI platforms supporting both large-scale training and real-time inference
- Build and maintain end-to-end ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring
- Implement robust CI/CD workflows for models, including versioning, rollback, testing, and observability
- Design monitoring systems to track model health, drift, accuracy, latency, and cost efficiency
- Design low-latency inference services with clearly defined SLAs
- Apply model optimization techniques such as compression, quantization, distillation, or hardware acceleration
- Balance performance, quality, and cost across different deployment environments
- Lead and grow a multidisciplinary team of ML engineers, MLOps specialists, and applied AI practitioners
- Establish execution standards that prioritize reliability, speed, and continuous improvement
- Mentor senior contributors and build strong technical ownership across the team
- Act as the primary bridge between AI research, product, and engineering teams
- Manage and prioritize a pipeline of AI initiatives moving from experimentation into production
- Contribute to long-term AI platform strategy, architecture decisions, and roadmap planning
- Partner with cloud and AI platform vendors to leverage advanced tooling and optimize infrastructure spend
Requirements
- 6+ years of experience building and operating production-grade AI or ML systems
- Proven track record of taking models from experimentation into large-scale, real-world deployment
- Strong grounding in machine learning fundamentals across training, inference, and evaluation
- Hands-on experience with MLOps practices, automation, and reliability engineering
- Deep familiarity with data pipelines, model monitoring, and observability frameworks
- Experience leading senior engineers or applied AI teams
- Strong systems-thinking mindset with the ability to own complex technical initiatives end-to-end
- Comfort operating in environments with ambiguity, fast iteration, and high expectations
- Excellent communication skills and the ability to align diverse stakeholders
- A strong sense of ownership, accountability, and technical judgment.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
AI systemsML pipelinesmodel optimizationCI/CD workflowsdata ingestionmodel monitoringinference servicesreliability engineeringautomationperformance thresholds
Soft Skills
leadershipmentoringcommunicationsystems thinkingownershipaccountabilitycollaborationproblem-solvingadaptabilitycontinuous improvement