Talent Acquisition Specialist

Location:

Experience:

Type:

by operating and configuring our AI recruiter (Fabric)

• Turn JDs into AI configs:

  Break down role requirements (skills, seniority, signals, red flags) into structured inputs, weights, and scoring rules for Fabric.

• Calibrate the model for each role:

  Provide few-shot examples, define knockout criteria, tune pass thresholds, and iterate until shortlists match hiring-manager expectations.

• Run AI-first pipelines end-to-end:

  Launch roles, monitor dashboards, review AI interview summaries, spot-check edge cases, and move high-signal candidates to final rounds.

• Quality & fairness oversight:

  Investigate false positives/negatives, refine prompts, and maintain consistent, bias-aware evaluation rubrics.

• Cheating detection supervision:

  Review AI flags, validate evidence, and adjust policies to keep signal clean.

• Sourcing via platforms (as needed):

  Use

  LinkedIn Recruiter

  and

  Naukri Recruiter

  to top up the funnel; feed sourced profiles back into Fabric for consistent evaluation.

• Partner with hiring managers:

  Run quick role-intake sessions, align on success criteria, and deliver weekly hiring readouts with clear next actions.

• Measure and improve:

  Track time-to-first-screen, shortlist precision/recall, onsite-to-offer, source effectiveness; run small experiments to improve conversion.

• Intake-to-first shortlist in

  ?48 hours

  for priority roles.

• ?80%

  of applicants screened end-to-end by AI with your calibrated rubric.
• Shortlist quality that hiring managers rate

  ?8/10

  on relevance.
• A simple playbook for repeating configurations across similar roles.

• 24 years

  in

  technical recruiting

  (e.g., Backend/Frontend/Full-stack, Data/ML, QA, DevOps/SRE, Platform), with clear closures you can speak to.
• Hands-on with

  LinkedIn Recruiter

  and

  Naukri Recruiter

  (projects, advanced Boolean, talent mapping)comfortable sourcing when needed.
• Strong grasp of how to

  assess engineering roles

  (signals in resumes/portfolios, what good looks like at 03/35/58 years, common pitfalls).

• Product mindset

  and clear writing: you can turn messy requirements into crisp prompts, rules, and checklists.

• Data literacy:

  comfortable reading dashboards, comparing cohorts, and making changes based on evidence.

• Startup experience

  and bias for speed; Bangalore market exposure is a plus.

• Familiarity with ATS/CRM workflows and clean documentation habits.
• Basics of

  JSON/Google Sheets

  ; light SQL is a plus.
• Experience running structured interviews or building scorecards.

• A front-row seat to AI-driven recruiting, with real ownership of outcomes.
• Direct access to founders and product; your feedback ships.
• Competitive salary with growth path into Lead/Head of TA (AI Ops).