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Sr. Machine Learning Engineer

PinnacleU
Bengaluru, Karnataka

Quick apply

Job details

Full-time
₹35,00,000 - ₹44,00,000 a year
1 day ago

Qualifications

Azure
Law
Master's degree
Machine learning
Distributed systems
Systems engineering
AI

Full job description

Position: Sr. Machine Learning Engineer

Location: Bangalore

Working Days: 5(Mon-Fri)

Experience Required: 3-5Years

Why this role exists

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-efficient 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?

Quick apply

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