Senior AI Engineer Team Lead Intern - STWGB (February 2025 Start)

• This is an unpaid internship at this time and is suitable for new recently graduated Master's or PhD. candidates that wants to be a Senior Deep Learning Engineer Team Lead.***
• This is an unpaid internship at this time and is suitable for new recently graduated Master's or PhD. candidates that wants to be a Senior Deep Learning Engineer Team Lead.***
• This is an unpaid internship at this time and is suitable for new recently graduated Master's or PhD. candidates that wants to be a Senior Deep Learning Engineer Team Lead.***
  
  

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 5-star support of a cloud-based enterprise AI platform with patent pending artificial intelligence, deep learning, and other core proprietary technologies awaiting patent approval. Flow Turbo™, the company's first product, is a brand of next-generation SaaS AI sales prospecting platform that is designed to maximize the productivity day-to-day for B2B sales representatives within B2B outbound, inbound, and inside sales organizations of B2B companies. The company also provides world-class award-winning customer support, professional services, guidance, certifications, training, and advisory services. The company is headquartered in Austin, Texas and is registered in Delaware.

Position Overview

Flow is seeking highly experienced and highly dedicated Team Lead - Senior Deep Learning Engineer Interns to join our world-class engineering organization, and oversee a scrum team in the development and deployment of world-class AI solutions. As a team lead, you will manage an experienced and dedicated group of engineers, and also provide clear engineering direction to ensure that AI models and solutions are developed, tested, and deployed properly. This role is designed for individuals with extensive engineering experience in artificial intelligence, deep learning, and back-end engineering. As a Team Lead - Senior Deep Learning Engineer Intern, you will work on AI solutions that push the boundaries of artificial intelligence, directly contributing to the development of innovative AI models and algorithms, as well as architecting robust cloud-based infrastructure for live production deployment. Your work will push the boundaries of real-world AI deployment, incorporating advanced models like Natural Language Processing (NLP), Large Language Models (LLMs) (such as RAG-based models, BERT, DistilBERT, RoBERTa, and LLaMa), and Deep Reinforcement Learning to drive next-generation AI capabilities.

In this role, you will oversee a team that is responsible for architecting AI models, algorithms, and cloud-based infrastructure for live production environments. You will take ownership of the technical roadmap, coordinate tasks, and mentor direct reports, while ensuring that the team adheres to agile principles and scrum methodologies. As a team lead, you will drive the entire AI development lifecycle, leading your team in designing, developing, and deploying next-generation deep learning models for real-time production environments. This includes hands-on model development with bleeding-edge NLP models, LLMs like RAG, BERT, RoBERTa, and LLaMa, as well as deep reinforcement learning. You will also coordinate with cross-functional Scrum teams to integrate these models into Flow’s back-end systems and cloud infrastructure, and oversee end-to-end AI pipeline engineering.

In this role, you will collaborate with world-class engineering and R&D teams, engaging in every phase of the AI development process. You will design, develop, integrate, train, and optimize deep learning models, create novel AI algorithms, and integrate complex AI systems into production environments. This internship will enable you to work with cutting-edge AI frameworks and cloud services, perfecting your skills in AI deployment, back-end system integration, and infrastructure management. This position is ideal for individuals seeking a career in senior AI engineering and interested in working with a pioneering company in enterprise AI solutions.

In this position, you will engage heavily in the development of natural language processing (NLP) models, large language models (LLMs) like RAG, BERT, DistilBERT, RoBERTa, and LLaMA, as well as deep reinforcement learning techniques to create AI systems that learn, adapt, and evolve. You will be directly responsible for building AI pipelines, refining training methodologies, and implementing real-time solutions, ensuring they are secure, reliable, and optimized for performance in live cloud environments. In this role, you will be at the forefront of Flow's R&D initiatives, engaging in the entire AI development lifecycle. You’ll design, implement, and refine AI and deep learning models using state-of-the-art frameworks such as TensorFlow, PyTorch, and Keras, with a focus on Natural Language Processing (NLP), Large Language Models (LLMs) including RAG, BERT, DistilBERT, RoBERTa, and LLaMA, and advanced neural architectures dedicated to Flow’s SaaS AI sales solutions. You’ll leverage these models in a cloud-based infrastructure, ensuring they are optimized, scalable, and capable of handling high-availability production loads.

LLM-Specific Feature Optimization For Enhanced Contextual Relevance

Candidates must demonstrate extensive previous experience in advanced feature engineering for optimizing large language models (LLMs) and Retrieval-Augmented Generation (RAG) architectures, specifically within high-dimensional vectorized environments. Expertise should encompass the following technical capabilities:

• Expert in engineering token embeddings, positional encodings, and contextual attention mechanisms tailored to transformer-based architectures (GPT, T5, BERT) within RAG frameworks. Candidates should have hands-on experience with feature engineering pipelines that enhance the contextual accuracy of LLMs for long-sequence, multi-turn conversational modeling.
• Advanced experience with embedding manipulation techniques, such as masked token prediction, continuous token augmentation, and variational encoding, to dynamically adjust feature weights based on semantic proximity, particularly in dense retrieval tasks.
• Mastery of reinforcement learning paradigms for adaptive LLM fine-tuning, utilizing reward functions derived from similarity scores and relevance metrics in high-frequency retrieval systems to improve query-response fidelity.
  
  

Complex Vector Database Integration & Feature Structuring for Semantic Similarity:

• Extensive experience designing and engineering feature vectors for semantic search optimization within vector databases (e.g., Pinecone, Faiss, Weaviate) and high-dimensional vector space management. Candidates should have technical proficiency in high-dimensional distance metrics such as cosine similarity, Euclidean distance, and inner product, optimized for specific retrieval tasks.
• Expert in structuring custom embeddings and vector schemas that enhance semantic search precision by capturing latent contextual signals through PCA, UMAP, and t-SNE techniques. Expertise in embedding normalization, vector scaling, and dimension reduction to balance retrieval speed and vector alignment accuracy.
• Ability to construct and manipulate ANN (approximate nearest neighbor) indices, such as Hierarchical Navigable Small World (HNSW) graphs and IVF (Inverted File) structures, to optimize high-throughput search scenarios. Experience in designing vector clusters using k-means and density-based algorithms, enhancing semantic granularity across vectorized search spaces.
  
  

High-Precision Semantic & Similarity Search Engineering

• Mastery in designing and implementing hybrid search architectures that combine dense embeddings with sparse vectors (BM25, TF-IDF) for enhanced retrieval relevance across semantic layers. Experience integrating hybrid dense-sparse models in RAG systems, ensuring that each query achieves optimal precision and recall based on adaptive similarity metrics.
• Expert in developing and tuning semantic similarity metrics, particularly cosine similarity, for real-time high-volume similarity search tasks. This includes leveraging cosine-similarity-based scoring mechanisms in similarity search pipelines to refine response ranking and ensure contextual relevance.
• Experience constructing cosine similarity feature transformations to boost search accuracy in query expansion contexts, utilizing cosine-based re-ranking and feature recalibration strategies that enable real-time refinement of relevance scoring.
  
  

Specialized RAG-Specific Feature Engineering For Retrieval Optimization

• Expertise in engineering feature pipelines within RAG architectures, specifically for enhancing query augmentation and retrieval conditioning based on multi-stage RAG frameworks. Candidates should have experience structuring bi-encoder and cross-encoder embeddings to support context-dependent token weighting and real-time relevance adjustments.
• Advanced experience in developing dynamic re-ranking mechanisms, integrating cosine and dot-product similarity metrics within RAG query layers for optimized retrieval at both coarse-grained and fine-grained levels. Proficiency in implementing memory-efficient vector stores and cached retrieval pathways that ensure low-latency response in high-frequency applications.
• Ability to optimize retrieval via custom feature weighting models, which selectively prioritize features based on semantic relevance derived from query intent prediction models, response context preservation, and adaptive relevance feedback mechanisms.
  
  

Embedding & Similarity Feature Optimization With Real-Time Systems

• Expert in designing high-dimensional embedding structures that support rapid cosine similarity calculations, especially within sparse or sparse-dense hybrid retrieval models. Familiarity with optimization techniques that reduce the computational load of similarity calculations in production-grade environments.
• Expertise in constructing advanced indexing schemes (e.g., IVF, PQ) that facilitate high-speed similarity search across large vector stores while minimizing precision loss. Skilled in implementing and tuning complex scoring layers, including custom cosine similarity scoring models that account for context-switching in multi-turn LLM interactions.
• Demonstrated capability in applying real-time re-ranking protocols within RAG-based systems, incorporating cosine-similarity-driven reordering, redundancy reduction in query results, and relevance fine-tuning based on LLM contextual embeddings.
  
  

In addition to AI architecture and AI development, you will be responsible for engineering and implementing end-to-end AI pipelines, ensuring proper integration of these AI models into back-end systems developed using Django. You will be heavily involved in the integration of AI with back-end systems using Django, enabling flawless interactions between AI models and other system services. You will design, build, and manage APIs to support AI functionalities, ensuring efficient data exchange and smooth operations. Your expertise in AI model-backend integrations will extend to building and managing robust APIs that deliver novel AI capabilities, ensuring all systems are highly scalable, secure, and efficient. You will collaborate with cross-functional Scrum teams to drive the evolution of Flow's AI solutions, solve complex problems, and ensure that every deployment meets stringent performance and security standards.

You'll also implement stringent quality assurance (QA) practices, leveraging test-driven development (TDD), unit testing, integration testing, regression testing, API testing, and continuous integration/continuous deployment (CI/CD) pipelines. You will have the opportunity to lead cutting-edge research and development in deep learning, working with a forward-thinking engineering organization in a dynamic, remote-first environment. This is an unparalleled opportunity for recent graduates or individuals holding a Master’s degree in Computer Science or Artificial Intelligence who has extensive experience with AI engineering, deep learning, and eager to hone their expertise in back-end integration with state-of-the-art frameworks like Django. The internship is remote-only and requires a commitment of at least 30 hours per week, and provides the chance to gain invaluable hands-on experience in AI engineering within a high-pressure, technically rigorous environment.

• MUST BE ABLE TO COMMIT STAYING AT THE COMPANY FOR AT LEAST A BARE MINIMUM OF 6 MONTHS.***
  
  

Key Responsibilities

• Managing a Scrum Team:
• Lead an experienced and dedicated Scrum team, providing strong engineering direction, managing direct reports, and facilitating sprint planning to meet sprint goals.
• Mentoring Team Members: Offer clear engineering guidance to fellow engineers, and answering technical engineering questions and resolving engineering blockers.
• Setting Engineering Standards: Define best practices, quality standards, and efficient engineering standards for AI model development, integration, testing, deployment, and delivery.
• Scrum Process Management: Implement and manage Agile methodologies, ensuring the team maintains productivity, meets deadlines, and delivers high-quality AI solutions.
• Novel AI Modeling & Development:
• Design, develop, and implement novel AI models using TensorFlow, Neural Networks, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), Chatbots, and Deep Reinforcement Learning.
• Develop and optimize deep learning algorithms tailored to specific project needs.
• Semantic search, similiarity search, cosine similarity.
• Advanced featuring engineering.
• Conduct AI training, fine-tuning models for high accuracy, and performance optimization.
• Back-End Integration:
• Flawlessly integrate AI models with back-end systems using Django and related technologies.
• Develop and maintain APIs for AI services, ensuring they are robust, scalable, and efficient.
• Collaborate with back-end engineers to ensure smooth AI and back-end integration.
• Cloud Deployment & Infrastructure:
• Deploy AI models to cloud platforms, configuring cloud infrastructure for optimal performance in live production environments.
• Manage cloud infrastructure services for deployment, including server setup, networking, and security.
• Ensure that AI models are scalable, secure, and maintainable in a cloud environment.
• Testing & QA:
• Implement QA practices, including unit testing, integration testing, regression testing, and API testing, to ensure the reliability of AI models and their integrations.
• Expert with Test-Driven Development, and SonarQube.
• Use continuous integration and continuous deployment (CI/CD) pipelines to automate testing and deployment processes.
• Collaboration & Innovation:
• Work closely with cross-functional teams, including data scientists, engineers, and product managers, to develop innovative AI solutions.
• Participate in brainstorming sessions and contribute to the evolution of AI strategies within the organization.
  

Qualifications

• Education: Recently graduated with a Master's degree, or PhD. in Computer Science or Artificial Intelligence.
• Experience: 5 years of professional experience in AI engineering, with a focus on novel AI architectures, novel AI modeling, deep learning, vector databases, semantic search, and feature engineering, and back-end engineering.
• Technical Skills:
• Expert with Django for back-end engineering, AI development, AI pipelines, and API integration.
• Expert in AI frameworks such as TensorFlow, PyTorch, and Keras.
• Expert in Deep Learning, Neural Networks, NLP, LLMs, vector databases, vector data, vectorizer models, model fine-tuning, and Deep Reinforcement Learning.
• Expert in Retrieval Augmented Generation models (RAG-based models).
• Expert in advanced feature engineering.
• Expert with cloud platforms for deploying AI models in live production environments.
• Expert with QA practices, including unit testing, integration testing, regression testing, and API testing.
• Remote Work: Must be able to work remotely and dedicate a minimum of 30 hours per week.
• Time Commitment:
• MUST BE ABLE TO DEDICATE AT LEAST 30 HOURS PER WEEK TO THIS POSITION.
• MUST BE ABLE TO STAY AT THE COMPANY FOR AT LEAST 6 MONTHS.
  

Benefits

• Remote native; Location freedom
• Professional industry experience in the SaaS 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.

• This is an unpaid internship at this time and is suitable for new recently graduated Master's or PhD. candidates that wants to be a Senior Deep Learning Engineer Team Lead.***
  
  

Please send resumes to services_admin@flowai.tech