About Chargeblast
Chargeblast is a dynamic and innovative technology company at the forefront of leveraging cutting-edge machine learning and artificial intelligence to solve complex problems and deliver impactful solutions. We are passionate about pushing the boundaries of what's possible in the AI space, creating sophisticated platforms that empower businesses and drive progress. Our team thrives on collaboration, creativity, and a relentless pursuit of excellence, working together to build intelligent systems that shape the future.
What You'll Do
- Design, develop, and maintain robust, scalable machine learning infrastructure and backend services using Python, FastAPI/Django, and AWS.
- Lead the development and deployment of advanced LLM-powered AI agents, leveraging frameworks like LangGraph and LangChain for complex orchestration.
- Architect and implement sophisticated Retrieval Augmented Generation (RAG) systems to enhance model accuracy and relevance.
- Establish and refine comprehensive evaluation methodologies, including automated testing, LLM-as-judge, and A/B experimentation, for generative AI outputs.
- Collaborate with cross-functional teams to integrate ML solutions into production systems, ensuring high performance, reliability, and maintainability.
What We're Looking For
- Experience: 5+ years of professional experience in Machine Learning Engineering, with a strong focus on backend development and AI systems.
- Backend & Architecture Expertise: Proficient in Python with extensive production experience using frameworks like FastAPI or Django. Solid understanding of distributed job processing (e.g., Celery, AWS SQS) and database technologies (PostgreSQL, MongoDB).
- LLM & Agent Development: Demonstrated production experience with large language model APIs (OpenAI, Anthropic) and frameworks for agent orchestration (LangGraph, LangChain). Experience with RAG systems, embedding models, and advanced prompt engineering is essential.
- ML System Evaluation: Proven ability to design and implement rigorous evaluation strategies for generative AI, including dataset creation, LLM-as-judge techniques, and A/B testing of ML pipelines. Familiarity with relevant evaluation platforms is a plus.
- Cloud & Observability: Hands-on experience with cloud platforms, particularly AWS, and familiarity with observability tooling (e.g., Datadog, Sentry, OpenTelemetry, Grafana) for monitoring and debugging ML systems.
- Problem-Solving & Collaboration: A proactive approach to problem-solving, strong analytical skills, and the ability to work effectively in a collaborative, remote-first environment.
Tech Stack
Python, FastAPI, Django, AWS, PostgreSQL, MongoDB, OpenAI, Anthropic, LangGraph, LangChain
What's In It For You
- 💰 Competitive compensation
- 🌎 Remote-first culture
- 📈 Growth opportunities
- 🤝 Work with cutting-edge technology
Ready to join? Apply now and let's build something amazing together.