AI Machine Learning Engineer (AI / ML: Python / Go) We're seeking a highly motivated AI / Machine Learning Engineer who thrives at the intersection of data science and backend engineering - someone who can take a model from notebook to production, and architect intelligent systems in Go and Python that scale to millions of requests. The ideal candidate is a self-starter who independently identifies opportunities, experiments with new approaches, and ships production-ready solutions without constant direction. Key Responsibilities AI / Machine Learning • Research, design, and deploy machine learning models across NLP, time-series forecasting, and event detection domains. • Build LLM-driven systems (e.g. summarization, RAG pipelines, embedding search) optimized for financial news and quantitative data. • Develop model serving APIs and scalable inference layers using Go or Python. • Implement model monitoring, drift detection, and continuous retraining pipelines. • Work with financial text (earnings call transcripts, filings, news) to extract structured insights. • Collaborate with data engineers to build training datasets, feature stores, and embedding databases. Backend & Infrastructure • Develop and maintain high-performance Python or Go microservices that integrate with AI systems and Go data APIs. • Design and optimize real-time inference pipelines on AWS, leveraging ECS/EKS, S3, and Lambda. • Ensure low-latency, fault-tolerant, and scalable delivery of AI-powered data. • Implement CI/CD for ML workflows, including containerization, automated deployment, and versioning. • Partner with DevOps to manage cloud infrastructure and ensure robust observability for AI workloads. Required Qualifications • 4+ years of experience in AI/ML or data engineering roles, with a proven track record of deploying ML models in production. • Computer science degree (Bachelor minimum) • Deep proficiency in Python (data, ML) and Go (backend, microservices). • Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face. • Experience with transformer architectures, embeddings, or fine-tuning LLMs. • Strong understanding of data pipelines, feature extraction, and model lifecycle management. • Familiarity with Docker, Kubernetes, and AWS (EKS, S3, Lambda, EC2). • Excellent problem-solving skills and ability to work independently in a distributed environment. Preferred Skills / Experience • Startup experience. • Financial services or fintech background • Experience building LLM-powered APIs or retrieval-augmented generation (RAG) systems. • Knowledge of vector databases (e.g., Pinecone, Weaviate, FAISS, OpenSearch kNN). • Experience with Kafka, LangChain, or data streaming architectures. • Familiarity with financial data systems, real-time analytics, or news NLP. • Exposure to MLOps tools (MLflow, BentoML, SageMaker, Airflow, etc.). • Contributions to open-source ML or Go projects are a strong plus. Tech Stack • Languages: Python, Go • ML Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain • Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM) • Containers & Orchestration: Docker, Kubernetes • Data & Streaming: Kafka, Postgres, OpenSearch • CI/CD: GitHub Actions, GitLab CI • Monitoring: Datadog, Prometheus, Grafana • Version Control: Git (Gitlab / Github) IMPORTANT Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it.