7+ years of experience building, deploying, and operating reliable data and ML systems in production environments
BS/MS/PhD in Computer Science, Data Science, Engineering, or a related field
Strong proficiency in writing maintainable, production-quality Python code
Deep knowledge of AI/ML algorithms, including clustering, supervised/unsupervised learning, natural language processing (NLP), and large language models (LLMs)
Extensive hands-on experience writing SQL and working with databases
Experience with AI/ML observability, monitoring, and evaluation frameworks
Advanced understanding of CI/CD pipelines (CircleCI, Harness) and API development best practices with FastAPI
Proven experience mentoring and guiding engineering teams, with a track record of driving complex projects in dynamic environments
You will design and develop LLM-powered AI solutions that incorporate RAG, tool calling, structured outputs, as well as knowledge and memory management.
You will oversee the deployment and monitoring of machine learning systems in production environments.
You will optimize and scale inference for both real-time and batch processing, ensuring low-latency and cost-efficient AI execution.
You are an owner of quality by evaluating models/agents, both offline and online, as well as implementing and maintaining production observability tools.
You will build and contribute to scalable data and ML infrastructure.
You will collaborate with cross-functional teams to align AI development with business needs.
You are adaptable and comfortable with the ambiguity of working in a highly dynamic agentic/LLM development space, evaluating emerging technologies and methodologies to continuously enhance system performance.
You ensure adherence to security standards and data governance best practices.
6–10 years in a software engineering individual contributor role
Hands-on experience (or strong interest) in working with LLMs through prompt engineering, LangChain / LangGraph (or similar agentic libraries), integrating LLM-based reasoning, RAG, vector embeddings, and designing and implementing knowledge graphs