To candidate (words from Head of AI): You will join our cross-functional AI team to build, fine-tune, and productionize AI applications that combine frontier LLMs, computer-vision models, and cloud-native services. The ideal candidate has shipped end-to-end solutions - from data curation and model training to scalable, secure deployments on AWS, GCP or Azure.
We are building a culture within ASC that values ownership, experimentation, and open research.
Responsibilities
Build Production AI Systems - Develop enterprise-grade AI applications: conversational systems, RAG solutions, document intelligence, multimodal applications, .... with modern frameworks. Responsible for building fine-tuning pipelines for Computer Vision models
Design Technical Architecture - Create effective AI engineering strategies, evaluation frameworks, middleware layers, and specialized caching systems for optimal performance. Technical analysis of business requirements to implement the application/system
Develop End-to-End Solutions - Build complete AI applications from scratch while also integrating with existing systems through APIs and service interfaces. Design, build, review reliable BE and FE code for Generative AI application which will be deployed in Cloud based or on Premises
Qualifications
2-5 years of AI/ML engineering, including at least ≥ 1 year each in LLM and computer-vision projects.
Strong Python, PyTorch (or TensorFlow/Keras) and software-engineering best practices (testing, code review).
Proven experience deploying models on at least one major cloud:
AWS: SageMaker, EKS/ECS, S3, Lambda, CloudWatch.
GCP: Vertex AI, GKE, Cloud Storage, Cloud Functions, Logging.
Azure: Azure ML, AKS, Blob Storage, Functions, Monitor.
Hands-on with MLOps tools: Docker, Kubernetes, MLflow/W&B, DVC, GitHub Actions (or similar).
Working with knowledge of vector databases (FAISS, Milvus, Qdrant) and REST/gRPC micro-services.
Bachelor's in CS, EE, Applied Math or related field
Benefits
Offer upto 65M + Signing Bonus (case by case)
Work on advanced AI systems: Build production AI agents and explore emerging approaches to agent orchestration, context engineering, and evaluation.
AI-first engineering environment: Work with modern AI development tools and help establish engineering practices designed around human-AI collaboration.
High technical ownership: Influence architecture, engineering standards, development workflows, and technology decisions from an early stage.
Direct collaboration with technical leadership: Work closely with experienced technical decision-makers with minimal organizational overhead.
Remote working arrangement: Work remotely with a distributed international team, with required overlap during US Central mornings.
Professional growth: Gain hands-on experience with emerging agentic engineering practices, advanced LLM systems, and production AI infrastructure.
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