ML- AI Engineer

About thinkbridge

thinkbridge is how growth-stage companies can finally turn into tech disruptors. They get a new way there – with world-class technology strategy, development, maintenance, and data science all in one place. But solving technology problems like these involves a lot more than code. That’s why we encourage think’ers to spend 80% of their time thinking through solutions and 20% coding them. With an average client tenure of 4+ years, you won’t be hopping from project to project here – unless you want to. So, you really can get to know your clients and understand their challenges on a deeper level. At thinkbridge, you can expand your knowledge during work hours specifically reserved for learning. Or even transition to a completely different role in the organization. It’s all about challenging yourself while you challenge small thinking.

thinkbridge is a place where you can:

1. Think bigger – because you have the time, opportunity, and support it takes to dig deeper and tackle larger issues.
   
2. Move faster – because you’ll be working with experienced, helpful teams who can guide you through challenges, quickly resolve issues, and show you new ways to get things done.
   
3. Go further – because you have the opportunity to grow professionally, add new skills, and take on new responsibilities in an organization that takes a long-term view of every relationship.
   

thinkbridge.. there’s a new way there. ™

Why This Role Is Different

• Architect end-to-end ML/AI solutions that actually work in production
  
• Build and maintain production-grade systems with proper monitoring, alerting, and continuous improvement
  
• Make strategic technical decisions on approach, tools, and implementation
  
• Translate complex AI concepts into business value for clients
  
• Set technical direction for project teams through architecture and best practices
  
• Stay current with AI research and identify practical applications for client problems
  

If your beliefs resonate with these, you are looking at the right place!

• Accountability –Finish what you started
  
• Communication–Context aware, pro-active, and clean communication
  
• Outcome –High throughput
  
• Quality –High-Quality work and consistency
  
• Ownership –Go Beyond
  

• Strong Python proficiency with production ML experience
  
• Hands-on experience deploying AND maintaining ML systems in production
  
• Experience with both GenAI (LLMs, RAG systems) and classical ML techniques
  
• Understanding of ML monitoring, drift detection, and model lifecycle management
  
• Cloud deployment experience (Azure knowledge helpful; AWS experience highly valued)
  
• Containerization and basic MLOps practices
  

Good to have technical skills

• Experience fine-tuning open-source models to match/beat proprietary models
  
• Advanced MLOps (CI/CD for ML, A/B testing, feature stores)
  
• Published work (papers, blogs, open-source contributions)
  
• Experience with streaming/real-time ML systems
  

What We're Really Looking for

Beyond technical skills, we need someone who:

• Takes initiative and drives projects without waiting for instructions
  
• Has actually felt the pain of their own technical decisions in production
  
• Can explain "why this approach" to both engineers and business stakeholders
  
• Thinks critically about when to use (and when NOT to use) GenAI
  
• Has opinions about ML best practices based on real experience