Senior Deep Learning Engineer Intern (June 2025 Start)

Company Overview:

Flow Global Software Technologies, LLC., operating in the Information Technology (IT) sector, is a cutting-edge high-tech enterprise AI company that engages in the design, engineering, marketing, sales, and blue chip service of cloud-based AI platforms, Flow Nebula™ and Flow Turbo™, with advanced artificial intelligence, deep learning, large-scale data systems, and other proprietary technologies. The company also provides world-class award-winning customer support, professional services, and advisory services. The company is headquartered in the United States and is registered in Delaware.

The core of Flow’s work is advanced engineering in artificial intelligence, deep learning, and large data systems design. The company focuses in the development of bleeding-edge AI platforms for B2B sales success, with a focus on scalable, high-performance, and secure infrastructure. This includes robust back-end architecture, well-defined microservices patterns, and modern data-driven design principles applied across every engineering layer.

Flow's AI architecture leverages state-of-the-art advancements in deep learning, advanced LLM-based workflows, transformer networks, and techniques in natural language processing and understanding (NLP/NLU). The engineering organization builds on semantic modeling principles, incorporating graph theory, vector databases, and graph databases to create structured, high-context reasoning systems. These components are connected together through clearly defined data flows and schema designs that are enforced through precise database ER modeling.

The engineering organization applies a modular, event-driven approach using Apache Kafka to support a distributed microservices environment. This architecture allows for high-throughput messaging, real-time data handling, and fault-tolerant service interactions. Data propagation and event orchestration are controlled through well-defined Kafka topics and message contracts to ensure consistency across the platform.

In terms of implementation, Flow utilizes a polyglot technology stack. Languages such as Rust, Go/Golang, Python, C++, and C# are selected based on product-specific requirements related to performance, concurrency, and system-level control. FastAPI and gRPC are used for API exposure and inter-service communication, enabling low-latency, high-reliability operations across distributed components.

Security, observability, and reliability are embedded throughout the engineering process. Code is structured for maintainability and correctness, with strong emphasis on static typing, performance profiling, and automated testing pipelines. Proprietary technologies are developed with rigorous documentation, review protocols, and architecture validation processes.

Flow Global Software Technologies, LLC. maintains a focused engineering discipline, and a highly technically intense engineering culture dedicated to advancing artificial intelligence and future-generation AI platforms for B2B sales success without compromise.

Position Overview:

The Senior AI Engineer Intern position is intended for highly experienced, dedicated, and committed engineers with advanced AI architecture, systems engineering, and distributed computing expertise. This role involves working with systems that demand real-time performance, high reliability, and modular service design. Core backend technologies include Rust and Go, selected for their concurrency models, low memory footprint, and execution speed. Python is used in parallel for machine learning pipeline integration, inference layer orchestration, and working with existing open-source AI frameworks. Senior AI Engineer Intern must have experience developing microservices with gRPC and Protobuf for inter-service communication and be familiar with deploying applications in Docker containers orchestrated by Kubernetes or K3s. Core understanding of service observability using Prometheus, Grafana, and Loki is expected for monitoring performance metrics, logging, and distributed tracing.

To be considered for the Senior AI Engineer Intern role, candidates must demonstrate advanced proficiency in at least two or more of the following programming languages: Rust, Go, and Python. In systems-level development, Rust is prized for its compile-time guarantees around memory safety, zero-cost abstractions, and asynchronous concurrency using frameworks such as Tokio. Senior AI Engineer Interns should possess a clear understanding of Rust’s ownership model, lifetime annotations, trait system, and how to write idiomatic asynchronous functions and multi-threaded tasks that leverage nonblocking I/O. Go, on the other hand, is leveraged for its simplicity, built-in concurrency using goroutines and channels, and straightforward garbage-collected runtime. Interns should be comfortable building microservices in Go, exposing gRPC endpoints defined by Protocol Buffers, and fine-tuning resource usage for optimal performance. Python remains indispensable for AI pipeline integration, model prototyping, and glue code, particularly when working with deep learning frameworks, data preprocessing libraries, or orchestration tools. Advanced experience in Python should include familiarity with virtual environments, dependency management (e.g., pip, poetry, or conda), and writing production-quality code that uses linters (flake8, black) and type checking (mypy). Experience with asynchronous Python frameworks—such as FastAPI paired with Uvicorn or Hypercorn—will be valuable when building inference servers or lightweight API gateways for model serving.

A high level of proficiency is required in both low-level and high-level programming environments. Senior AI Engineer Interns must demonstrate fluency in systems-level languages such as Rust and Go, leveraging their performance, concurrency, and memory safety features to build efficient backend services and orchestration layers. In parallel, strong experience with Python is essential, particularly for building and deploying AI inference pipelines, integrating open-source AI libraries, and managing model-serving workflows using tools like FastAPI, Uvicorn, or Triton Inference Server. Expertise with ONNX, TorchScript, and model optimization tooling is considered a significant asset. Senior AI Engineer Interns should also be experts in the principles of inference graph compilation, tensor-level optimization, and serving models in low-latency containerized environments.

In addition to Rust and Go, Python remains a critical language for model integration, data processing, and glue code. Senior AI Engineer Interns should have experience building and maintaining Python-based inference servers using frameworks like FastAPI, and Uvicorn. Senior AI Engineer Interns need have experience in asynchronous programming in Python (async/await, event loops), and be able to write code that efficiently handles concurrent requests, performs CPU- or GPU-bound tasks, and integrates with external dependencies such as deep learning frameworks. A solid grasp of virtual environments, dependency management (pip, Poetry, or conda), and testing frameworks (pytest, unittest) is necessary. Interns will write Python scripts to preprocess data, extract features, and coordinate model serving pipelines that rely on tools such as PyTorch, TensorFlow, ONNX Runtime, or Triton Inference Server. They should be familiar with model serialization formats (e.g., TorchScript, ONNX) and know how to convert and optimize models for production use. Experience with Python libraries for data manipulation—such as pandas, NumPy, or Apache Arrow—will allow Senior AI Engineer Interns to develop feature extraction and data transformation routines that feed into AI inference pipelines.

Senior AI Engineer Interns will also be involved in deploying and scaling AI models in production environments. Experience working with ONNX, TorchScript, or TensorFlow SavedModels is useful for understanding how to convert and optimize models for inference. Expertise with serving models using APIs built with FastAPI, Triton Inference Server, or other lightweight inference engines is required. Senior AI Engineer Interns should understand batching strategies, model caching, and request timeouts for synchronous and asynchronous inference requests. Senior AI Engineer Interns will also work with model acceleration tools such as ONNX Runtime, TensorRT, or other CPU/GPU optimized runtimes. Engineers are expected to evaluate performance trade-offs for quantization, pruning, or distillation, and assess their impact on latency, throughput, and accuracy. The ability to profile model-serving endpoints and identify bottlenecks using tools like Py-Spy, perf, or NVIDIA’s Nsight is highly valuable.

This position also requires experience in modern data systems. Senior AI Engineer Interns must have extensive experience working with relational databases such as PostgreSQL or SQLite, and be familiar with vector databases like Qdrant, Weaviate, ChromaDB, or FAISS for similarity search and embedding storage. An expert understanding of vector indexing, approximate nearest neighbor search (ANN), and real-time embedding pipelines is necessary. Experience with graph databases such as Neo4j or ArangoDB is beneficial, especially in modeling relationships or querying large entity graphs. Senior AI Engineer Interns should also be experts in using MinIO or other S3-compatible object storage systems for managing large-scale unstructured data and artifacts. Experience in ingestion and event-streaming systems such as Redis Streams, Kafka, or NATS is also necessary.

Developing distributed microservices is at the core of this position. Senior AI Engineer Interns must be comfortable designing, implementing, and testing gRPC services using Protocol Buffers as the interface definition language. They will write .proto files that define RPC endpoints, message schemas, and service contracts, and generate code stubs in Rust, Go, and Python. Interns should understand the nuances of streaming RPCs versus unary RPCs, how to handle deadlines and timeouts via gRPC contexts, and how to implement server-side and client-side interceptors for purposes such as authentication, logging, and metrics collection. Senior AI Engineer Interns need to build RESTful API wrappers around existing gRPC services using API gateways or sidecar proxies, and need be experts with tools like Envoy, Traefik, or NGINX for edge routing. Understanding API versioning strategies—such as semantic versioning, URL or header versioning—and backward compatibility practices is essential to ensure smooth service evolution. Senior AI Engineer Interns will also be expected to build robust client libraries for internal services, writing code that handles retries, exponential backoff, and proper error propagation to support fault-tolerant service communication.

Senior AI Engineer Interns will also design and maintain data ingestion and processing pipelines. Experience with Redis Streams, Kafka, or NATS is mandatory for building event-driven architectures that decouple producers from consumers. Senior AI Engineer Interns should understand how to partition Kafka topics, manage consumer groups, and ensure message ordering and delivery guarantees (e.g., at-least-once, exactly-once). Senior AI Engineer Interns in this role need to be able to write services that consume events, process payloads, enrich metadata, and update downstream systems. Experience in serialization formats such as Protobuf, Avro, or JSON is required to ensure data consistency across heterogeneous services. Expertise with ETL and ELT patterns, including tools like Airflow, Dagster, or Prefect, is also mandatory for scheduling and orchestrating batch and real-time data workflows.

A strong background in distributed systems and container orchestration is required. Senior AI Engineer Interns must be experts in Docker and Kubernetes, including writing Helm charts or raw manifests, managing CI/CD pipelines, and deploying horizontally scalable services. Experience with load balancing and routing tools such as Traefik, Envoy, or HAProxy is beneficial. Senior AI Engineer Interns should be capable of deploying and scaling services across staging and production environments and managing service configuration in GitOps workflows. Understanding of container security, RBAC, secrets management, and sandboxing is also expected.

A well-rounded candidate will demonstrate expertise with observability and monitoring stacks, including Prometheus for metrics collection, Grafana for visualization, and Loki or the ELK stack for log aggregation and analysis. Experience with distributed tracing tools and an understanding of system health diagnostics are highly valued. Senior AI Engineer Interns should be able to diagnose performance bottlenecks, monitor system throughput, and tune services for reliability under load.

Professional industry experience with open-source AI libraries and toolchains is essential. This includes working with transformer-based models via libraries like HuggingFace Transformers, llama.cpp, or vLLM, and deploying those models in production workflows. Senior AI Engineer Interns should be comfortable writing APIs that interface with these models and handling pre/post-processing of input data. Additionally, experience with OCR libraries such as Tesseract, EasyOCR, or PaddleOCR, as well as tools like OpenCV, is required. Experience with GUI automation tools like pyautogui, xdotool, enigo, or rdev is necessary, especially for integrating with non-API environments. Experience with headless or virtualized runtime environments such as QEMU, Firecracker, or Xvfb is mandatory.

Language Proficiency and Infrastructure Stack

Senior AI Engineer Interns are expected to be fluent in multiple programming languages and able to apply each in its optimal context. Rust is used where memory safety and parallel performance are essential, particularly in low-level runtime components and systems that require predictable latency. Go is applied to backend services where simplicity, concurrency, and runtime efficiency are priorities. Python remains essential for working with AI frameworks, including model serving via tools such as FastAPI, Uvicorn, or Triton Inference Server. Experience with TorchScript, ONNX, and model conversion or acceleration tools is preferred. Expertise with deploying inference workloads in containerized environments is required, along with basic proficiency in infrastructure-as-code tools such as Terraform, Ansible, or Nix. Candidates should also be comfortable working with GitOps pipelines and CI/CD automation practices.

The technical foundation for this role includes substantial experience with multiple programming languages and an understanding of where each language best fits within the system. Senior AI Engineer Interns are expected to be proficient in Rust, which is frequently used in performance-sensitive, memory-constrained, or concurrent execution contexts. Familiarity with Rust’s ownership and borrowing model, lifetimes, trait-based generics, and asynchronous runtimes such as Tokio is important. Interns should be able to write idiomatic, testable Rust that prioritizes safety and predictability. Additionally, Go (Golang) is a key part of the stack due to its efficiency, simplicity, and suitability for building microservices. Senior AI Engineer Interns should understand Go’s concurrency primitives (goroutines, channels, and select), context propagation, error handling patterns, and the effective use of interfaces. Senior AI Engineer Interns must also be fluent in Python, particularly for tasks involving machine learning, data orchestration, or scripting model-serving APIs. Comfort with Python’s async capabilities, packaging systems, virtual environments, and integration with common AI libraries (e.g., PyTorch, Hugging Face Transformers) is expected. Engineers will often work across Rust, Go, and Python codebases simultaneously, and the ability to reason about cross-language boundaries is essential.

Data Systems and Storage Technologies

Senior AI Engineer Interns will work across multiple layers of the data stack. For structured data and transactional workloads, familiarity with PostgreSQL or SQLite is required. For high-performance, similarity-based retrieval operations, knowledge of vector databases such as Qdrant, Weaviate, ChromaDB, or FAISS is strongly preferred. Candidates should understand the basics of vector indexing, approximate nearest neighbor (ANN) search, and embeddings. Additionally, familiarity with graph databases such as Neo4j or ArangoDB is valuable, especially in contexts where dynamic relationship modeling or complex entity graphs are involved. Experience working with MinIO or other S3-compatible object stores is beneficial for handling large binary assets and unstructured data at scale. Interns should also be able to implement and monitor high-throughput ingestion pipelines, leveraging Redis Streams, Kafka, or NATS for event streaming, buffering, and decoupled processing.

AI Toolchains

Hands-on experience with modern open-source AI toolchains is expected. This includes working with transformer-based models through libraries such as HuggingFace Transformers, llama.cpp, or vLLM. Senior AI Engineer Interns must be capable of loading, managing, and evaluating pre-trained models and interfacing with them through custom Python or REST APIs. They should also be experienced with concepts such as inference graph compilation, tensor optimization, and accelerated model serving via ONNX, TorchScript, or Triton. Knowledge of OCR libraries like Tesseract, EasyOCR, or PaddleOCR and basic usage of OpenCV is helpful when working on visual input processing. Experience using tools like pyautogui, xdotool, rdev, enigo, or SikuliX is critical, especially when integrating systems without API access. Understanding how to run GUI-based applications inside headless or virtualized environments using QEMU, Firecracker, or Xvfb is also necessary.

Deployment, Observability, and Systems Operations

Senior AI Engineer Interns are expected to contribute to infrastructure deployment, scaling strategies, and observability pipelines. They should be familiar with deploying services in both development and production environments using Docker Compose, Helm, or direct Kubernetes manifests. Load balancing and reverse proxy experience with tools like Traefik, Envoy, or HAProxy is beneficial for routing internal and external traffic across distributed services. Security practices such as role-based access control, container sandboxing, and credential management using tools like Vault or secrets-injected environments are considered essential. Senior AI Engineer Interns should be experts in using Prometheus for metric collection, Grafana for visualization, and Loki or ELK Stack for log aggregation and querying. Experience with service mesh configurations, fault-tolerant deployment strategies, and auto-scaling logic based on system health metrics will be considered a strong asset. Senior AI Engineer Interns must demonstrate a commitment to modularity, maintainability, and performance-driven engineering at every level of the tech stack.

 ***MUST BE ABLE TO FULLY COMMIT TO STAYING AT THE COMPANY FOR AT LEAST A VERY BARE MINIMUM OF 3 FULL MONTHS***

   Roles and Responsibilities

• Voluntary artificial intelligence research and development
• Backend System Development
  Build and maintain high-performance services in Rust and Go, emphasizing safe concurrency, low-latency I/O, and modular component design.
• Service Interface Design
  Define, implement, and evolve gRPC/Protobuf interfaces for inter-process communication, ensuring type safety, backward compatibility, and efficient serialization.
• AI Pipeline Integration
  Develop and optimize Python-based inference endpoints using FastAPI and Uvicorn. Implement data marshalling, batch handling, and monitoring hooks around AI model calls.
• Container Lifecycle Management
  Create and maintain container images with Docker, author deployment manifests for K3s or Kubernetes clusters, and contribute to GitOps-driven rollout pipelines.
• Data Storage & Retrieval
  Work with relational systems (PostgreSQL, SQLite), high-dimensional stores (Qdrant, Weaviate, FAISS), graph databases (Neo4j, ArangoDB), and streaming caches (Redis Streams) to architect data flows, index tuning, and query optimization.
• Messaging & Event Streaming
  Implement and manage event-driven flows using Kafka, NATS, or Redis Pub/Sub. Design robust publish/subscribe patterns, backpressure handling, and end-to-end schema validation.
• Infrastructure Instrumentation
  Configure and extend observability stacks (Prometheus for metrics, Loki for logs, Grafana for dashboards; optionally Jaeger/OpenTelemetry for tracing) to capture service health, performance metrics, and troubleshoot distributed pipelines.
• Configuration & Secrets Management
  Use declarative formats (YAML, TOML, JSON) and IaC tools (Terraform, Ansible, Nix) to manage cluster configurations, environment variables, and credential injection in a secure, auditable manner.
• Collaboration & Documentation
  Participate in peer code reviews, maintain clear API documentation, produce runbooks for operational procedures, and communicate design decisions across cross-functional teams.

 Technical Experience & Qualifications

• Education: Recently graduated with a Master's degree in Artificial Intelligence, Data Science, or Computer Science.
• Experience: 5-6+ years of professional industry experience in AI engineering, with a focus on novel AI architectures, novel AI modeling, deep learning, RAG, RAG pipelines, advanced LLM-based workflows, vector databases, semantic search, feature engineering, and back-end engineering.
• Programming Languages
  • Rust: systems-level modules, memory-safe concurrency
  • Go: microservice orchestration, concurrent data pipelines
  • Python: ML integration, API endpoint development (FastAPI, Uvicorn)
• Inter-Service Communication
  • gRPC & Protobuf: interface definition, schema evolution, performance tuning
• Containerization & Orchestration
  • Docker: multi-stage builds, image optimization
  • Kubernetes/K3s: manifest authoring, Helm/Kustomize, GitOps workflows
• Data Management
  • Relational: PostgreSQL, SQLite (schema design, indexing)
  • Vector Stores: Qdrant, Weaviate, FAISS (embedding retrieval, ANN tuning)
  • Graph DBs: Neo4j, ArangoDB (relationship modeling, traversal optimization)
  • Caching & Streams: Redis Streams, Kafka, NATS (event buffering, backpressure)
• AI Frameworks
  • Transformers & PyTorch: model integration, inference optimization
  • ML Serving: FastAPI, Uvicorn, ONNX/Triton basics
• Infrastructure-as-Code & DevOps
  • Terraform, Ansible, Nix: environment provisioning, configuration management
  • CI/CD: Git-based pipelines, automated testing, deployment gates
• Observability & Monitoring
  • Prometheus, Grafana, Loki: metric collection, dashboard creation
  • OpenTelemetry/Jaeger: distributed tracing fundamentals
• Version Control & Collaboration
  • Git: branching strategies, code review workflows
  • Documentation: Markdown, API schema docs, runbooks
• Soft Skills
  • Clear technical communication, both written and verbal
  • Self-driven, able to research new tools and propose improvements
  • Collaborative mindset in distributed, cross-discipline teams

• Remote Work: Must be able to work remotely and dedicate a minimum of 40 hours per week.
• Time Commitment:
  • MUST BE ABLE TO DEDICATE AT LEAST 40 HOURS PER WEEK TO THIS POSITION.
  • MUST BE ABLE TO STAY AT THE COMPANY FOR AT LEAST A VERY BARE MINIMUM OF 3 FULL MONTHS.

 Benefits:

• Remote native; Location freedom
• Professional industry experience in the tech and AI industry
• Creative freedom
• Potential to convert into a full-time position

 Note:

This internship offers an exciting opportunity to gain hands-on experience in AI engineering within a high pressure and innovative environment. Candidates must be self-motivated, proactive, and capable of delivering high-quality results independently. The internship provides valuable exposure to cutting-edge technologies and professional industry development practices, making it an ideal opportunity for aspiring senior AI engineers.