DEV Community: Pratik Mokashi

AI and ML Hiring in India for US Companies: Structuring the Search Across Time Zones

Pratik Mokashi — Fri, 06 Nov 2026 14:05:54 +0000

Introduction

US companies increasingly look to India for AI and ML talent. The talent is there, but the search needs to work across nine and a half to thirteen and a half hours of time difference. Here is how to structure AI ML hiring in India for US companies.

Define the role first

Decide whether you need an applied LLM engineer, an ML engineer, a data scientist or an MLOps engineer. Each needs a different search.

Build the loop around time zones

Async technical screens or short take-homes for early stages
Live system design and team conversations during the US morning, India evening
Final conversations with decision makers scheduled at fixed weekly slots

Assess production readiness

US teams often expect India AI engineers to work independently. Look for:

Shipped AI features with real users
Evaluation and monitoring practices
Clear written communication

Plan team structure

Many US companies start with two or three senior AI engineers and an India-based lead, rather than individual hires reporting to US managers.

Data and security

Agree in advance which data India engineers can access, and how. This often affects which roles can be based in India.

Compensation

AI talent in India is in high demand. Benchmark against global companies hiring in India, not only local startups.

Role definitions

For details on each AI role, see how to hire AI engineers in India.

Conclusion

US companies can hire strong AI and ML talent in India by defining the role precisely, building a time-zone-aware interview loop, assessing production readiness, planning team structure and benchmarking compensation carefully.

AI and ML Hiring in Pune: Finding Applied ML Talent in a Services-Heavy Market

Pratik Mokashi — Fri, 06 Nov 2026 13:56:45 +0000

Introduction

Pune has a large technology workforce, much of it in IT services. That can make it harder to find engineers with hands-on, production ML experience. For teams running AI ML recruitment in Pune, the trick is knowing where applied ML talent actually sits.

Where to look

Analytics and data science teams inside capability centers
Product companies building SaaS with ML features
Automotive and industrial companies working on computer vision, predictive maintenance or sensor data
Services teams that have delivered real ML projects for clients

Separating applied from theoretical

Many candidates list ML skills from courses or small projects. To find production experience, ask:

What model did you deploy, and where does it run?
How did you monitor it after launch?
What went wrong, and how did you fix it?

Consider adjacent profiles

Strong data engineers or backend engineers with some ML exposure can grow into ML engineering roles with the right support.

Interview design

Use a practical exercise focused on data preparation, evaluation and deployment trade-offs rather than algorithm trivia.

Role clarity

Be clear whether you need a data scientist, ML engineer or AI engineer working with LLMs. See how to hire data scientists in India for role definitions.

Conclusion

AI and ML hiring in Pune requires looking in the right places, from capability centers to industrial companies, and testing for production experience. Consider adjacent profiles and keep the role definition clear.

Oslo Energy-Tech and SaaS Companies Writing India Job Briefs

Pratik Mokashi — Fri, 06 Nov 2026 13:48:52 +0000

Introduction

Oslo's technology scene includes energy, maritime and climate tech alongside SaaS and fintech. These companies often need engineers who understand data-heavy, industrial or regulated domains. For Oslo companies hiring in India, a precise job brief is the best way to attract the right people.

Describe the domain clearly

Engineers may not know what energy trading, grid optimisation or vessel data platforms involve. Explain the domain in a few plain sentences and what makes it interesting.

Be specific about the data

Many energy-tech roles involve time-series data, sensor data or large simulations. State the volume, sources and tools so data and backend engineers can judge their fit.

Define the stack, but focus on fundamentals

List the main technologies, but emphasise the core skills: data modelling, distributed systems, reliability and testing.

Explain the impact

Energy and climate work can be a strong draw. Describe how the role contributes to real outcomes.

Clarify working arrangements

State overlap hours with Oslo, the work model in India and how the team is organised.

Keep requirements realistic

Domain experience is helpful but not always necessary. Strong engineers can learn a domain if the team supports them.

Data roles

Many Oslo companies start with data engineering. See how to hire data engineers in India for role definitions and interview design.

Conclusion

Oslo energy-tech and SaaS companies can write better India job briefs by explaining the domain, describing the data, focusing on fundamentals, highlighting impact, clarifying working arrangements and keeping requirements realistic.

Amsterdam Tech Companies Planning India Hires Around Notice Periods

Pratik Mokashi — Fri, 06 Nov 2026 13:29:39 +0000

Introduction

Amsterdam companies are used to European notice periods of around one month. In India, notice periods for experienced engineers are often longer, commonly two to three months. For Amsterdam companies hiring in India, this changes how hiring plans and project timelines should be built.

Why notice periods are longer

Many Indian employers set long notice periods in employment contracts, especially for mid and senior roles. Some allow early release, but it is not guaranteed.

Effects on your plan

A role opened today may not have a person working until three or four months later
Candidates may accept other offers during the notice period
Projects planned around a start date can slip

How to plan around it

Start hiring earlier. Open roles one quarter before you need the person.

Ask about notice periods early. Include it in the first screening call.

Consider buyout support. Some companies help candidates buy out part of their notice period. Decide your policy upfront.

Stay in touch. Keep candidates engaged between offer and joining with onboarding materials, team introductions and regular check-ins.

Have a backup. For critical roles, keep a second candidate warm until the first one joins.

When you need someone sooner

If you need someone faster, candidates who are already serving notice or between roles may be an option, but assess them as carefully as anyone else.

Conclusion

Notice periods in India are often much longer than in the Netherlands. Amsterdam companies should open roles earlier, ask about notice periods early, decide on buyout policies, keep candidates engaged and maintain backups for critical roles. The same planning applies to most European companies hiring in India.

Founding Engineers in India: Who Fits a Pre-Product-Market-Fit Team

Pratik Mokashi — Fri, 06 Nov 2026 13:14:21 +0000

Introduction

A founding engineer is not just an early employee. They shape the codebase, the culture and often the product itself. For startups looking to hire founding engineers in India, the right profile looks quite different from a typical senior engineer at a large company.

Traits that matter before product-market fit

Range: comfortable across backend, frontend, infrastructure and data
Speed with judgment: ships quickly but knows where shortcuts are dangerous
Customer curiosity: wants to talk to users and understand their problems
Comfort with ambiguity: can work without detailed specs
Ownership: treats the product as their own

Backgrounds that often work

Early engineers at other startups, even ones that did not succeed
Engineers from small product teams inside larger companies
Builders with side projects that real users adopted

Backgrounds to examine carefully

Engineers from very large organisations can be excellent, but some are used to specialised roles, mature tooling and large support teams. Ask how they would work without those.

Interview approach

A paid or short practical project that resembles real early-stage work
A conversation about a product decision they influenced
A discussion of the technical debt they would accept and avoid

Compensation and equity

Founding engineers usually expect meaningful equity and clarity on vesting, role scope and decision rights. Be transparent early.

After the first few hires

As the team grows, you will add specialists. A structured engineering and AI hiring plan helps you move from generalists to a balanced team.

Conclusion

Founding engineers in India should bring range, speed with judgment, customer curiosity and ownership. Look beyond big-company titles to people who have built from zero, and be transparent about equity and decision rights. The right founding engineer will set the standard for everyone who follows.

Data Engineer Hiring in India: Pipelines, Platforms and Interview Design

Pratik Mokashi — Fri, 06 Nov 2026 13:14:03 +0000

Introduction

Data engineering sits under almost every analytics and AI initiative, yet it is often the hardest team to hire for. Job titles vary widely, and many candidates have experience with only one part of the stack. This post covers how to hire data engineers in India with a clear brief and a fair interview.

Know which data engineer you need

Pipeline engineer: ingestion, transformation, orchestration and data quality
Platform engineer: warehouses, lakehouses, streaming infrastructure and cost control
Analytics engineer: modelling data for analysts, often with SQL-first tools

Small teams often need someone who can cover pipelines and some platform work.

Skills worth testing

SQL depth: window functions, performance and data modelling
Batch and streaming trade-offs
Orchestration and failure recovery
Data quality checks and observability
Cost awareness on cloud data platforms

Interview design

Data modelling exercise. Give a realistic business scenario and ask for a schema. Discuss how it would change as requirements grow.

Pipeline design discussion. Ask how they would build a pipeline from a source system to a warehouse, including retries, late data and backfills.

Code review. Share a short transformation script with deliberate issues and ask for feedback.

Past incident walkthrough. Ask about a pipeline failure they handled and what they changed afterwards.

Avoid tool-only filtering

Specific tools change quickly. A candidate with strong fundamentals can learn a new orchestration or warehouse tool. Filter on concepts, not logos.

Fit with AI work

Many companies hire data engineers because of new AI projects. Make sure the role supports both analytics and ML needs. A broader engineering and AI hiring plan can align data engineering with ML engineering roles.

Conclusion

Data engineer hiring in India works best when you define the type of role, test fundamentals like SQL, modelling and failure recovery, and avoid filtering only on tools. Use realistic modelling and pipeline exercises rather than puzzles. Strong data engineers make every downstream team faster.

Engineering Recruitment in Mumbai: Where Fintech and Media-Tech Compete for the Same Engineers

Pratik Mokashi — Fri, 06 Nov 2026 12:46:24 +0000

Introduction

If you are an engineer in Mumbai, your recruiter inbox is probably full of fintech roles, trading platforms, payments companies and media or streaming products. If you are a hiring manager, you are competing with all of them. This post looks at engineering recruitment in Mumbai from both sides: what engineers care about, and how hiring teams can run a process that strong engineers actually finish.

The shape of the Mumbai engineering market

Mumbai's tech scene is closely tied to finance. That brings a lot of engineers with experience in:

High-throughput transaction systems
Low-latency services and market data
Security, audit trails and compliance-driven design
Payments, reconciliation and ledger systems

Media, entertainment and consumer internet add another layer: content delivery, recommendation systems and large consumer apps.

The result is a market with excellent backend and platform engineers, but also heavy competition for them.

What senior engineers in Mumbai look for

From an engineer's perspective, a role is attractive when:

The technical problem is clear. "Scale our platform" means nothing. "Move settlement from batch to near real time" means something.
Ownership is real. Engineers want to know what they will own end to end.
The interview respects their time. Long, unstructured loops lose good people.
The commute or work model is honest. Mumbai commutes are a real factor. Be clear about hybrid or office expectations upfront.

Designing a process that works

Write a problem-first job description. Lead with the system, the scale and the constraints. Put the tech stack second.

Keep the loop tight. A practical structure for a senior backend role:

Recruiter or hiring manager screen focused on past systems
One system design conversation based on a real problem you have
One code-focused session, either pair programming or a review of an existing PR
A team and values conversation

Assess the right skills. If you plan to hire backend engineers in India for a payments product, test idempotency, failure handling and data consistency. Do not default to puzzle questions.

Move fast once you have signal. In a competitive market, a week of silence after the final round often means losing the candidate.

Common mistakes

Copying a Bengaluru salary benchmark without checking Mumbai's finance-driven pay levels
Treating BFSI experience as a single category, when a trading systems engineer and a core banking engineer have very different strengths
Hiding the work model until the offer stage

Conclusion

Engineering recruitment in Mumbai works best when the role is framed around a real technical problem, the interview loop is short and relevant, and the work model is clear from the first conversation. Engineers in the city have plenty of options, so the teams that win are the ones that respect their time and explain the work honestly. If you are hiring here, start by rewriting your job description around the problem, not the stack.