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2.0 - 4.0 years
0 Lacs
bengaluru, karnataka, india
On-site
Talent Acquisition Manager (AI-First Recruiting) Location: Bengaluru (hybrid) Experience: 24 years | Function: Talent Acquisition (Technical) Type: Full-time | Role: Individual contributor (with scope to lead as team scales) Why this role Help us hire engineers and other technical roles by operating and configuring our AI recruiter (Fabric) . Instead of running a traditional, manual-heavy process, youll translate role requirements into AI configurations, prompts, rubrics, and thresholds so the tool can source, screen, interview, and prioritize candidates fasterwith you owning quality and outcomes. What youll do 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. What success looks like (first 90 days) 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. Youll thrive if you have 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. Nice to have 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. What we offer 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). Show more Show less
Posted 4 days ago
0.0 years
0 Lacs
bengaluru, karnataka, india
On-site
Talent Acquisition Associate Location: Bengaluru (hybrid) Experience: 02 years | Function: Talent Acquisition (Technical) Type: Full-time Why this role Join a recruiting team that uses Fabric (our AI recruiter) to screen and prioritize candidates at scale. Youll learn how to turn role requirements into AI configurations, keep pipelines moving, and ensure hiring managers see strong, ready-to-interview shortlists fast. What youll do Configure roles in Fabric: Translate JDs into clear skills/experience signals, knockout criteria, and pass thresholds; iterate based on hiring-manager feedback. Review AI shortlists: Spot-check interviews/summaries, tag edge cases, and escalate mismatches; maintain high signal-to-noise. Top-up sourcing (as needed): Run searches on LinkedIn Recruiter and Naukri Recruiter , shortlist prospects, and feed them into Fabric for consistent evaluation. Candidate ops: Schedule interviews, manage updates, keep notes clean in ATS/CRM, and ensure quick turnarounds. Quality and metrics: Track time-to-first shortlist, stage conversions, and offer progress; share weekly updates. Employer brand basics: Draft clear job posts and concise outreach messages; support meetups or campus drives when required. What success looks like (first 90 days) Intake-to-first shortlist in ?72 hours for assigned roles. ?70% of applicants screened end-to-end by AI with your calibrated setup. Hiring-manager shortlist quality rated ?8/10 on relevance for your roles. Reliable weekly dashboards for pipeline health and next actions. Youll thrive if you have 02 years in recruiting or HR (internships/projects count); some exposure to technical hiring is a plus. Familiarity with LinkedIn Recruiter and/or Naukri Recruiter basics (Boolean, projects, saved searches). Curiosity and comfort with AI tools/workflows ; you enjoy testing prompts, rules, and checklists. Strong written notes, structured thinking, and follow-through. Startup exposure or the appetite to work in a fast, ambiguous environmentBengaluru market familiarity is a plus. Nice to have Understanding of common tech roles (Backend/Frontend, Data/ML, QA, DevOps/SRE) and what good looks like at juniormid levels. Google Sheets comfort (filters, pivot basics) and clean documentation habits. Experience with any ATS/CRM. What we offer Hands-on training in AI-driven recruiting and direct exposure to founders and hiring managers. Clear growth path into Talent Acquisition Manager (AI Ops) as we scale. Competitive salary; fast-moving, product-first culture. Show more Show less
Posted 4 days ago
1.0 - 5.0 years
1 - 2 Lacs
pune
Remote
6 months to 2 yrs of exp in resume sourcing & filter them quickly Proficient in using Naukri, LinkedIn & other sourcing platforms Basic understanding of job descriptions & candidate fitment Per-closure-based incentive/payment model (No Fixed Salary)
Posted Date not available
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