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About ByChat FinOps
ByChat FinOps is a production-ready B2B AI platform that helps organizations understand, analyze, and optimize cloud infrastructure costs across AWS.
Our platform combines cloud billing data with Generative AI, enabling users to ask natural language questions such as:
Why did our cloud costs increase this month?
Which services generate the highest expenses?
Will we exceed our budget next quarter?
Where can we reduce infrastructure costs?

Behind the scenes, intelligent LLM Agents work with cloud APIs, read-only SQL queries, billing datasets, forecasting models, anomaly detection algorithms, and FinOps analytics to deliver accurate, explainable business insights.
ByChat FinOps is an actively growing production product where AI reliability, security, scalability, and measurable business value are our highest priorities.

About the Role
We are looking for a Senior / Lead ML Consultant to support our engineering team on a part-time basis and provide expert guidance on the architecture, development, evaluation, and optimization of our AI/ML capabilities.
This is primarily an advisory and technical leadership role rather than a full-time hands-on engineering position.
You will work closely with our AI/ML engineers and technical team, review existing approaches and processes, identify potential risks and inefficiencies, and provide practical recommendations for improving the quality, reliability, and efficiency of our AI systems.
We expect you to act as a senior technical advisor and sparring partner for the team, helping us validate technical decisions and ensure that our ML/LLM practices meet production-grade standards.

What You'll Do
Your responsibilities will include:
Review the architecture and implementation of our AI/ML and LLM-based solutions.
Provide regular technical feedback to AI/ML engineers and help validate key engineering decisions.
Review existing ML/LLM development processes and recommend improvements.
Evaluate the effectiveness, reliability, and scalability of current AI workflows.
Advise the team on the design of LLM Agents, sub-agents, tool calling, structured outputs, and agentic workflows.
Review approaches to prompt engineering, context management, guardrails, validation, retries, and hallucination mitigation.
Assess and improve LLM evaluation methodologies, automated testing, golden datasets, and quality metrics.
Advise on AI observability, tracing, monitoring, and production quality control.
Review Text-to-SQL approaches and help ensure safe and reliable interaction with financial datasets.
Provide guidance on anomaly detection, forecasting, and other ML components used within the platform.
Identify performance bottlenecks and opportunities to optimize LLM latency, token usage, infrastructure usage, and operational cost.
Help the team evaluate new AI/ML technologies, frameworks, models, and architectural approaches before introducing them into production.
Participate in technical discussions and architecture reviews for complex AI-related features.
Mentor engineers and help strengthen the team's ML/LLM engineering practices.
Help establish practical engineering standards and best practices for building production-grade AI systems.

What We Expect
Must Have
Strong senior-level commercial experience in Machine Learning / AI Engineering, ideally including technical leadership or consulting responsibilities.
Proven experience designing and reviewing production AI/ML systems.
Strong practical experience with modern LLM applications and Agentic AI systems.
Deep understanding of:LLM Agents and agentic workflows
Prompt Engineering
Function / Tool Calling
Structured Outputs
Context management
Hallucination mitigation
Guardrails and validation
Retry and fallback strategies

Strong understanding of ML/LLM evaluation methodologies and how to measure AI system quality in production.
Experience with AI observability, tracing, testing, and monitoring.
Strong Python and SQL knowledge with the ability to review implementation and architecture decisions.
Understanding of statistics, anomaly detection, forecasting, and data-driven system evaluation.
Experience identifying technical risks, architectural weaknesses, and performance bottlenecks.
Ability to translate complex technical findings into clear, practical recommendations for an engineering team.
Strong communication and mentoring skills.
English sufficient for technical discussions, documentation, and regular communication with the team.

Nice to Have
Previous experience as a Lead ML Engineer, ML Architect, AI Architect, Staff/Principal ML Engineer, or ML Consultant.
Experience with LangChain, LangGraph, or other Agent frameworks.
Experience with AWS Bedrock, Claude, or other managed LLM platforms.
Experience with FinOps, cloud billing, or cloud cost optimization.
Knowledge of:AWS CUR
AWS Cost Explorer

Experience with Langfuse, LangSmith, RAGAS, or similar AI observability and evaluation platforms.
Experience designing evaluation pipelines and golden datasets for LLM applications.
Experience with forecasting and time-series analysis.
Experience processing large financial or billing datasets.
Understanding of multi-tenant architectures and secure AI tool execution.
Experience optimizing LLM latency, token usage, and operational costs.

Technology Stack
Python 3.12
FastAPI
LangChain
LangGraph
AWS Bedrock
PostgreSQL
SQL
Langfuse
RAGAS
Docker
Next.js / TypeScript

Engagement Format
Part-time consulting engagement
Flexible involvement based on project and team needs
Regular technical reviews and consultations with the AI/ML team
Architecture and solution reviews for key AI/ML initiatives
Focus on technical guidance, quality assurance, process improvement, and mentoring rather than day-to-day feature implementation

Why Join Us?
Influence the technical direction of a real production AI product without committing to a full-time role.
Work on challenging problems at the intersection of Generative AI, Agentic AI, Cloud, and FinOps.
Help shape the architecture and engineering practices behind production-grade AI systems.
Work with a team that values measurable AI quality, reliability, security, and business impact.
Apply your senior-level expertise where it has the highest impact: technical decisions, architecture, evaluation, optimization, and team development.
Stay close to modern AI technologies while working in a flexible consulting format.

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