AI Machine Learning Engineer (AI ML Python Go)

About Benzinga

Benzinga is a fast-growing financial media and data technology company reshaping how investors access information. We combine artificial intelligence machine learning and real-time data pipelines to surface insights before they hit the mainstream. Our platforms deliver structured news sentiment analytics and financial data APIs used by leading banks fintechs and AI companies worldwide.

Were 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)

Why Join Benzinga

• Build and ship production AI systems that shape how financial markets understand information.
• Operate with full creative freedom explore experiment and execute your ideas end-to-end.
• Work with a lean highly technical team where initiative and ownership are celebrated.
• Fully remote high-trust environment that rewards curiosity speed and execution.

IMPORTANT

Along with your application I want to hear about the most exceptional product youve built. Include a Loom video walking me through the product the code and explain the most significant challenge you faced when working on it.