Determining My Early Career Direction
What AI Won’t Replace
When I started using vibe coding, I realized something profound was happening. No wonder so many PMs keep saying they’ll replace programmers—I’ve actually seen colleagues with zero experience ship web apps.
So where does that leave us CS graduates?
AI suggests focusing on complex tasks or domain-specific work. I’m not particularly interested in business logic, so let’s talk about tackling complexity instead. Think distributed systems, high concurrency, load balancing. I’m not entirely sure yet, but heading toward systems that more people use and that sit at the core of infrastructure seems like the right call. I need to consciously steer my work in this direction.
Small-scale software development and frontend work feel increasingly pointless.
Infrastructure
I’ve suddenly become obsessed with infrastructure. I like applying the barbell strategy to engineering work with high certainty.
It’s a massive field, so I had AI help me categorize it (see the table at the end).
Personally, I feel infrastructure engineering is deeply technical, and the systems you build need to be genuinely complex. However, I don’t want to go deep into hardware—I don’t have much foundation there. AI recommends roles like AI Infrastructure Engineer, ML Platform Engineer, DevOps Engineer, Cloud Architect, or AI Operations Engineer.
Plus, infrastructure skills transfer well from tech companies to quant dev roles. I just need solid financial markets knowledge, which is exactly what I’m planning to spend Saturdays learning.
Current Focus
Learning complex applications while looking for opportunities to work on complex system architecture.
Dedicating an hour daily to personal project preparation.
Stay Open
Keep an open mind.
Sometimes interesting new products—like browsers or VS Code—are happy accidents. You can’t plan everything rationally; you need intuition too. Everything is change.
AI Infrastructure Roles Breakdown
| Role | Responsibilities | Tech Stack | Languages |
|---|---|---|---|
| AI Hardware Engineer | Configure and manage hardware resources; design and optimize AI computing platforms (GPU, TPU, FPGA) | Hardware: NVIDIA GPU, Google TPU, Intel FPGA Optimization: CUDA, cuDNN, TensorRT Monitoring: nvidia-smi, GPU-Z | C/C++: hardware interfaces & optimization Python: hardware interaction Verilog/VHDL: hardware design |
| AI Infrastructure Engineer | Design and manage compute and storage resource allocation; ensure efficient AI workload execution | Cloud: AWS, GCP, Azure, OpenStack Resource Management: Slurm, Kubernetes Containerization: Docker, Kubernetes | Python: automation & cloud APIs Go: infrastructure tools Bash/Shell: automation scripts |
| Data Engineer | Design, develop and optimize data pipelines; handle large-scale storage and stream processing for AI training | Big Data: Apache Spark, Hadoop, Flink ETL: Airflow Databases: PostgreSQL, MySQL, BigQuery, Snowflake | Python: data processing Scala: big data SQL: queries & management Java: big data frameworks |
| Networking Engineer | Manage network communication layers; optimize cross-node data transfer for low latency and high bandwidth | Protocols: gRPC, HTTP/2, WebSocket High-speed: RDMA, InfiniBand Load Balancing: Nginx, HAProxy | C/C++: protocol implementation Python: monitoring & automation Bash/Shell: network scripts |
| Storage Engineer | Design and optimize storage architecture; ensure efficient, scalable, and secure data persistence | Distributed Storage: HDFS, Ceph, Amazon S3 Databases: Cassandra, MongoDB, Redis Tools: DVC, ModelDB | Python: data & storage management Java: distributed storage systems Go: high-performance storage SQL: query optimization |
| ML Platform Engineer | Build and maintain ML platforms; support automated training, experiment tracking, and model versioning | Frameworks: TensorFlow, PyTorch, JAX, Keras Model Management: MLflow, TFX, DVC Distributed Training: Horovod, Ray, PyTorch Distributed | Python: ML algorithms & models Bash/Shell: automation Go: platform tools Java: distributed training |
| DevOps Engineer | Design and implement CI/CD workflows; automate deployment, updates, and maintenance of AI systems | Automation: Ansible, Terraform, Chef, Puppet CI/CD: Jenkins, GitLab CI, CircleCI Containerization: Docker, Kubernetes | Python: automation Bash/Shell: scripting Groovy: Jenkins pipelines Go: CI/CD tools |
| AI Operations Engineer | Operate AI systems; monitor model inference services, ensure high availability and optimize performance | Monitoring: Prometheus, Grafana, Datadog Logging: ELK Stack Serving: TensorFlow Serving, Triton, TorchServe | Python: monitoring & automation Bash/Shell: operations Go: ops tools Java: server optimization |
| Security Engineer | Ensure AI system security; protect data and models, prevent attacks, ensure compliance | Crypto & Auth: SSL/TLS, OAuth, JWT, AES Compliance: GDPR, HIPAA, SOC2 Container Security: Aqua Security, Twistlock | Python: security scripts C/C++: secure communication Go: container security Bash/Shell: auditing |
| Cloud Architect | Design and manage AI cloud architecture; ensure high availability, elasticity, fault tolerance, and security | Platforms: AWS, GCP, Azure Storage: S3, Google Cloud Storage, Azure Blob Tools: Terraform, CloudFormation | Python: automation & integration Go: resource management Bash/Shell: cloud ops Java: cloud applications |
| System Architect | Design overall AI system architecture; coordinate compute, storage, and network resources efficiently | Distributed: Apache Kafka, Spark, Kubernetes Design Tools: UML, ArchiMate Databases: SQL, NoSQL, GraphDB | Python: automation & optimization Go: distributed systems Java: large-scale architecture C/C++: low-level optimization |
| AI Research Engineer | Research and develop AI algorithms; propose new methods and optimizations, improve existing models | Frameworks: TensorFlow, PyTorch, JAX Languages: Python, C++, R Optimization: Optuna, Hyperopt, Ray Tune | Python: algorithm development C++: performance optimization R: statistical analysis Julia: high-performance computing |