Find work that matches your evidence, not only your keywords.
Your resume records proof. Your LinkedIn profile makes that proof discoverable. A useful job search converts both into role families, title variants, skills, domains and constraints, then searches several channels deliberately.
Before opening a job board
If a recruiter searched for five reasonable versions of your target role, which words in your profile would make you appear?
Build a search fingerprint from your evidence
| Profile signal | Question | Example |
|---|---|---|
| Primary role | What role can I defend today? | Machine Learning Engineer |
| Adjacent roles | Which nearby titles use the same evidence? | Applied Scientist, Data Scientist–ML, AI Engineer |
| Problems | What have I actually solved? | Forecasting, ranking, churn, anomaly detection |
| Methods | Which methods can I explain and implement? | Gradient boosting, NLP, experimentation, time series |
| Production evidence | Can I move beyond a notebook? | APIs, batch pipelines, monitoring, cloud, SQL |
| Domain evidence | Where does my experience reduce hiring risk? | Fintech, healthcare, retail, logistics |
| Constraints | What must be true for the role to work? | India, remote eligibility, experience level, compensation |
Use evidence twice: put it in the profile so recruiters can find you, and put it in your searches so you can find employers asking for it.
Expand titles without losing the role
Companies name similar work differently. Search the work described in your resume, not only the title printed on it.
Model-building
Data Scientist, Applied Scientist, Decision Scientist, Research Engineer.
Product ML
Machine Learning Engineer, AI Engineer, Applied ML Engineer.
Production ML
MLOps Engineer, ML Platform Engineer, ML Infrastructure Engineer.
Specialist ML
NLP Engineer, Computer Vision Engineer, Forecasting Scientist.
Search broad, apply narrow. Broad discovery reveals title variation. Narrow selection preserves application quality.
Choose the channel
Would you use the same platform to find an early-stage AI startup, an Indian product company, and a production-ML infrastructure role?
Useful platforms beyond the obvious job boards
Keep LinkedIn, Naukri and selected company career pages as broad discovery channels. Add two specialised platforms that match your target market; more accounts do not automatically create a better search.
HiringCafe
Best for: structured discovery across public listings. Filter by seniority, salary, company size, industry, funding stage and working conditions.
Simplify
Best for: profile-based matches, job tracking and reducing repetitive forms. Use assisted autofill with human review.
Wellfound
Best for: startup and technology roles. Useful signals include salary, equity, startup stage and direct founder or hiring-team access.
YC Startup Jobs
Best for: roles at Y Combinator companies. Read the company and founders before applying; early teams often value evidence of ownership.
Instahyre
Best for: matched technology opportunities in India. Resume, preferences and work experience influence the opportunities shown.
Cutshort
Best for: Indian technology and product hiring, including data science. Keep skills, experience and role preferences precise.
AIJobs
Best for: AI-specific discovery across ML engineering, data science, research, robotics, computer vision and AI product roles.
MLOps Community Jobs
Best for: production-ML, platform, infrastructure and operations roles where deployment evidence matters.
Platform names are leads, not endorsements. Features and listing quality change. Verify each vacancy on the employer’s official careers site, confirm location and work-authorisation rules, and never pay to apply.
Search where companies publish first
Many companies publish to an applicant-tracking system before an aggregator indexes the role. Search official career pages and common ATS domains directly:
Then open the employer’s careers site and verify that the role is still active. The official listing is the source of truth for requirements, location and application.
A five-stage discovery funnel
| Fit dimension | Weight | Fast question |
|---|---|---|
| Work and outcomes | 40% | Do I want the actual weekly work? |
| Defensible evidence | 30% | Can I prove the important requirements? |
| Constraints | 20% | Do location, level, authorisation and compensation align? |
| Interest | 10% | Would I still want this role without the company name? |
A sustainable weekly search system
Monday: calibrate
Review search results. Add useful title variants and remove noisy terms.
Three short searches
Check saved searches and alerts while roles are recent, not through one exhausting weekly session.
Track the funnel
Record source, fit score, version used, contact, status and follow-up date.
Optimise qualified conversations, not application count. If many applications produce no screens, inspect targeting and evidence before sending more.
Before counting keywords
What work is hidden inside this sentence: “Build and deploy churn models, partner with product teams, define success metrics, and monitor model performance”?
Read a JD in five layers
Outcome
What must increase, decrease, predict, automate or improve?
Weekly work
What will the person build, analyse, deploy, present or maintain?
Evidence
Which outcomes, scale, ownership and technical depth must be demonstrated?
Environment
Domain, data, stack, stakeholders, product maturity and constraints.
Interview prediction
What will the company probably test to trust the candidate with this work?
Worked JD analysis
| Signal | Interpretation | Preparation |
|---|---|---|
| Build and deploy | End-to-end ownership, not notebook-only modelling | Serving, pipelines, versioning, monitoring |
| Churn | Binary classification with intervention and label-window choices | Imbalance, leakage, thresholds, calibration |
| Partner with product | Predictions must support an action | Business framing and stakeholder story |
| Define success metrics | Offline accuracy is not enough | Business, model and experiment metrics |
| SQL and experimentation | Data extraction and causal product measurement | SQL practice and A/B testing |
Alignment decision
| Requirement | My evidence | Gap | Action |
|---|---|---|---|
| Deploy classification systems | Batch fraud scoring project | No online serving | Prepare batch design honestly; study online trade-offs |
| Define metrics | Threshold chosen from cost matrix | Weak online experiment story | Revise experiment design |
| Cloud platform | Used object storage and scheduled jobs | No Kubernetes | Do not claim Kubernetes; decide whether it is truly required |
Apply based on evidence and growth, not a perfect keyword match. Ask whether you want the actual work, can prove adjacent evidence, and can close the important gaps.
Prompt for honest resume tailoring
Platform research notes
Platform descriptions were checked against official candidate pages, documentation and job boards on 8 October 2026. Useful references: Wellfound for candidates, Instahyre candidate help, Cutshort FAQ, Simplify overview, HiringCafe documentation, YC jobs, AIJobs, and MLOps Community Jobs.