Most companies are using LLMs. Very few are building an advantage from them.
Right now, LLM cost is our largest margin constraint, and model behavior is still too generic to be defensible.
This role exists to:
- Turn LLM usage into a cost-productive system
- Build compounding intelligence across accounts
- Create a differentiated analysis layer that competitors can’t replicate
What you’ll do
You will not just build models. You will own the intelligence and cost structure of the platform.
1. Drive down LLM cost dramatically
- Reduce cost per interaction from 40 2 within 6 months
- Implement:
- Model tiering (right model for the right task)
- Caching strategies (semantic + response caching)
- Batching and async processing
- PTU / reserved capacity optimization
- Ensure performance does not degrade while reducing cost
2. Optimize infrastructure spend
- Reduce cloud spend from 20L/month 4L/month
- Work across infrastructure layers (Azure / compute / inference)
- Balance:
- Latency
- Cost
- Throughput
- Treat infra as a first-class optimization problem
3. Build the fine-tuning and learning pipeline
- Design systems where: Every interaction improves future performance
- Build pipelines for:
- Fine-tuning
- Feedback loops
- Continuous model improvement
- Ensure the 5th customer deployment is structurally better than the 1st
4. Create a differentiated intelligence layer
- Build analysis systems that:
- Extract signals from interactions
- Improve decision-making
- Drive outcome improvements
- Move beyond responses insight + action
5. Enable new AI-native product categories
- Identify opportunities where: AI enables workflows that were not previously possible
- Build foundational ML capabilities to unlock those categories
- Focus on creation, not just efficiency
6. Commoditize LLM usage internally
- Abstract complexity of LLM usage from product teams
- Build internal systems where:
- Cost is predictable
- Performance is consistent
- Make LLM usage a reliable utility layer
What success looks like
- Cost per interaction drops to 2 or lower
- Infrastructure spend reduces 5x without performance loss
- Model performance improves with every deployment
- Platform develops a clear intelligence advantage
- New AI-native capabilities become possible due to your systems
Who you are
- You have 3-5 years of experience in ML / applied AI / systems engineering
- You have worked with:
- LLMs
- Inference optimization
- Production ML systems
- You think in:
- Systems
- Trade-offs (cost vs latency vs quality)
- You care about real-world impact, not just model metrics
What will make you stand out
- Experience with:
- LLM optimization (prompting, fine-tuning, distillation)
- Distributed systems or infra-level optimizations
- High-scale inference systems
- Built systems that:
- Reduced cost significantly
- Improved performance over time
- Strong understanding of:
- Caching strategies
- Model routing
- Evaluation frameworks
Why join
- You will directly impact company margins and scalability
- Your work defines whether we have a defensible ML advantage
- You will build systems that move from: Generic AI usage compounding intelligence
What this role is not
- Not research-only
- Not experimentation without production impact
- Not isolated from product and business outcomes
What this role is
- A builder of ML systems at scale
- A driver of cost and performance optimization
- A creator of long-term competitive advantage
One question to self-evaluate
Can you build ML systems that get cheaper, smarter, and more valuable with every interaction?
📌 Sr. Machine Learning Engineer (India)
🏢 PinnacleU
📍 India
Reply to this offer
Impress this employer describing Your skills and abilities, fill out the form below and leave Your personal touch in the presentation letter.