Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, , TC FS
Tasks
- Define AI delivery roadmap
- Design AI agents and retrieval systems
- Drive delivery across complex programs
- Ensure responsible AI compliance
- Establish engineering ways of working
- Implement LLM RAG evaluation frameworks
- Lead cloud architecture and security design
- Lead end-to-end AI system delivery
- Manage squad dependencies and risks
- Mentor engineers and develop talent
- Own product discovery and MVP framing
- Set up MLOps LLMOps pipelines
Perks/Benefits
- Client site travel
- Competitive compensation benefits
- Continuous development programs
- Flexible working arrangements
- Hybrid working
- Innovation challenges
- Learning and certification support
Skills/Tech-stack
API | AWS | Azure | By Design | CI/CD | Cassandra | Cloud Native | Computer Vision | Databricks | Distributed Systems | Docker | Drift monitoring | ELT | ETL | Embeddings | Event Hubs | Event workflows | Faiss | GAN | GCP | GDPR | Generative Models | Governance | Graph Databases | Grounding | Hadoop | Hallucination mitigation | High Availability | Hybrid retrieval | IAM | Kafka | Kubernetes | LLM Evaluation | LLMOps | Lineage | MLOps | Machine Learning | Microservices | Milvus | Model Monitoring | NLP | Neo4j | Observability | Pinecone | Privacy by Design | Prompt engineering | Python | Reinforcement Learning | Resilience Engineering | Responsible AI | Serverless | Spark | Statistical modeling | Telemetry | Transformers | TypeScript | VAE | VNets | Vector Databases | Zero Trust
Education
N/A
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