We are seeking an experienced ML Engineer to lead the development, deployment, and optimization of intelligent systems across our platforms. This is a remote, full-time role for someone who can contribute from day one and guide junior engineers on the team. Expertise in Gemini , RAG , and Langchain integration, as well as a background in computer vision, is highly valued. Role Responsibilities Design and deploy scalable machine learning systems using modern frameworks (TensorFlow, PyTorch). Architect data pipelines and optimize model serving in production environments. Collaborate with cross-functional teams to define ML-driven product features. Conduct rigorous testing, evaluation, and monitoring of ML models. Mentor junior engineers and participate in code reviews, tech discussions, and process improvements. Lead efforts in Gemini and RAG -based model deployments. Develop and implement solutions that incorporate Langchain for advanced NLP and machine learning capabilities. Work on computer vision tasks (e.g., object recognition, image classification, segmentation). Skills & Experience 4+ years of experience building and deploying machine learning solutions. Strong Python programming and deep understanding of ML algorithms. Hands-on experience with cloud ML tools (AWS SageMaker, GCP AI Platform, etc.). Solid knowledge of RESTful APIs, Docker, CI/CD pipelines, and distributed computing. Experience with Gemini and RAG architectures in machine learning workflows. Expertise in Langchain for improved AI systems. Proficiency in computer vision techniques, such as image classification, segmentation, and object detection. Preferred Tools & Technologies TensorFlow, PyTorch, FastAPI, Airflow. Docker, Kubernetes, GitHub Actions. Experience with NLP, recommendation systems, or time-series analysis is a plus. Familiarity with Gemini , RAG , and computer vision tools. Familiarity with PyTorch, TensorFlow, OpenCV, PubSub
Role Summary We are looking for a passionate Python developer to work on football video and data analytics systems using computer vision and deep learning. Key Responsibilities Build pipelines for video ingestion, object tracking, and tactical data extraction Apply CV models to game scenarios (e.g., player recognition, heatmaps, motion vectors) Collaborate with football analysts to translate tactical needs into technical tools Requirements 3+ years of Python development experience Proficiency with OpenCV, PyTorch, TensorFlow, and real-time inference systems Familiarity with YOLO, segmentation models, sports analytics tools a big plus Strong passion for football and understanding of tactics, formations, and gameplay
Role Summary We’re hiring two Full Stack Developers with strong AI/ML integration skills. One will reinforce the AI tech pod, helping operationalize ML pipelines and LLMs. Key Responsibilities Build full stack systems that integrate with ML pipelines, APIs, and data layers Collaborate with AI team on model deployment and UX for ML-backed products Implement scalable backend systems and modern, reactive frontend components Requirements 3–6 years of full stack experience; one role can be mid-level, the other senior Deep experience in LLM applications (OpenAI API, RAG systems, LangChain) Experience with modern stacks: TypeScript, React, Python/FastAPI, Postgres Strong DevOps familiarity: Docker, cloud infra, monitoring, CI/CD pipelines
Role Summary We're looking for a hands-on AI Tech Lead to architect and guide the development of cutting-edge AI/ML systems. This role blends deep technical ownership with team collaboration, with an emphasis on production-grade ML, real-time systems, and practical deployment strategies. Key Responsibilities Lead AI/ML architecture and infrastructure design, with a focus on performance and scalability Collaborate with Full Stack and Product teams to deploy ML models into production pipelines Guide research and experimentation cycles (LLMs, vision models, custom models) Mentor engineers and contribute to a high-performance AI engineering culture Requirements 6+ years in AI/ML engineering roles, 2+ in technical leadership Deep experience with LLMs, model fine-tuning, deployment (e.g., Triton, TorchServe, ONNX) Strong Python/ML stack: PyTorch, TensorFlow, HuggingFace, LangChain, etc. Strong systems thinking, with background in production-grade AI systems
Role Summary We’re looking for a seasoned Full Stack Engineer to lead the development of complex, scalable web systems and services. The role requires strong expertise in modern frontend and backend technologies, with hands-on experience in databases (both SQL and NoSQL) and API design. Exposure to data-heavy or AI-integrated systems is a plus. Key Responsibilities Architect and build scalable backend-heavy systems with robust data modeling and API design. Lead end-to-end development using modern frontend frameworks like React and Angular. Collaborate with cross-functional teams, including AI/ML teams, to integrate intelligent features. Drive best practices in database design for MongoDB, relational databases (SQL), NoSQL systems, GraphQL APIs, and optionally graph databases such as Neo4j. Take ownership of projects from system architecture to deployment and performance optimization. Requirements 5–8 years of experience in full stack or backend-heavy roles. Strong expertise in Node.js (Python experience is optional but preferred). Proficiency in React and Angular for frontend development. Solid experience with MongoDB, SQL, NoSQL databases, GraphQL, and knowledge of graph databases (e.g., Neo4j). Experience designing and developing real-time, data-heavy web applications. Nice to Have Exposure to Python (not mandatory but considered a plus). Experience with AI/ML-based products or streaming data systems.
Role Summary We are looking for a passionate Python developer to work on football video and data analytics systems using computer vision and deep learning. Key Responsibilities Build pipelines for video ingestion, object tracking, and tactical data extraction Apply CV models to game scenarios (e.g., player recognition, heatmaps, motion vectors) Collaborate with football analysts to translate tactical needs into technical tools Requirements 3+ years of Python development experience Proficiency with OpenCV, PyTorch, TensorFlow, and real-time inference systems Familiarity with YOLO, segmentation models, sports analytics tools a big plus Strong passion for football and understanding of tactics, formations, and gameplay Show more Show less
Before you apply, make sure that your resumes have access else your resume will NOT be considered. About the Role We are seeking a Senior AI/ML Engineer to design, fine-tune, and deploy large-scale AI/ML systems. You will work on QLoRA-based fine-tuning, VLLM inference, distributed training , and cloud-native deployments on platforms like AWS SageMaker and GCP Vertex AI Agent Builder . The role will also involve developing applied AI systems , including computer vision solutions using YOLO and OpenCV , and building scalable event-driven pipelines leveraging Cloud Pub/Sub . Key Responsibilities Fine-tune and optimize LLMs using QLoRA, PEFT, and Hugging Face Accelerators . Implement VLLM for efficient large-scale inference. Build distributed and parallel training systems (DeepSpeed, Ray, PyTorch DDP). Develop computer vision models using YOLO and OpenCV for real-world applications. Deploy and manage AI/ML models on AWS SageMaker, GCP Vertex AI , and other cloud MLOps platforms . Design and implement event-driven AI pipelines with Cloud Pub/Sub and other messaging systems. Define and track LLM and CV evaluation metrics (accuracy, factuality, hallucination rate, object detection performance). Integrate graph-based LLM tools for knowledge reasoning and multi-agent AI systems . Requirements 6+ years in AI/ML, with 3+ years in LLM and applied computer vision systems . Expertise in Python, PyTorch, Hugging Face, QLoRA/LoRA, VLLM, YOLO, and OpenCV . Experience with distributed systems, AWS/GCP cloud MLOps , and vector databases (FAISS/Milvus) . Familiarity with LangChain/LlamaIndex , agent-based AI frameworks , and event-driven architectures (Cloud Pub/Sub) . Strong track record of deploying AI/ML models into production at scale .