Add AI and ML capacity without a $200K senior hire

Your product needs AI features. Maybe it's integrating an LLM, building a recommendation engine, or setting up an inference pipeline. The work is real and clearly defined. But a senior ML engineer in the US or Europe costs $150,000 to $200,000 a year, and your stage doesn't justify that line item. So the AI work sits in the backlog while your generalist engineers try to figure it out between sprints.

The painful path

You've tried having your full-stack developers handle the AI work. They're smart people, but ML engineering is a different discipline. The prototype took twice as long as expected. The model performance is mediocre. Nobody on the team has experience with fine-tuning, MLOps, or production inference at scale.

You've looked at AI consultancies. They charge $200 to $400 an hour and deliver a proof of concept that your team can't maintain after the engagement ends. You've posted the role on job boards, but qualified ML engineers get poached faster than you can schedule interviews, and the salary expectations are outside your band.

So the AI features stay half-built, the product roadmap slips, and your competitors who can afford the talent move faster.

What changes with SoTalented

You get a dedicated mid-level AI/ML engineer working only for your company, from a premium SoTalented Satellite Office in India. They handle the integrations, fine-tuning, data pipelines, and inference work that your product needs. Not research. Not theoretical. Applied AI engineering with clear deliverables.

A dedicated AI/ML engineer starts around $2,500 to $3,500 a month, all in. That's a fraction of the local equivalent, and you get someone who's focused on your product full-time, not split across consulting clients. They work in your codebase, attend your standups, and participate in code reviews like any other engineer on the team.

First hire takes about 3 weeks. 30-day cancellation if the fit isn't right.

Let's be honest about what mid-level delivers: production integrations, API-based AI features, fine-tuning with established frameworks, and data pipeline setup. If you need novel research or cutting-edge model architecture, that's a different hire. But for 90% of startup AI work, mid-level is the right level.

What your team looks like now

Your AI/ML engineer handles:

• LLM integration and prompt engineering for product features

• Model fine-tuning using established frameworks (PyTorch, TensorFlow, Hugging Face)

• Data pipeline setup and feature engineering

• Inference API deployment and optimization

• ML experiment tracking and model versioning

• Documentation and handoff so the work isn't stuck in one person's head

Ready to move AI off the backlog?

Hire an AI/ML Engineer and we'll match you with someone who fits your stack and scope.