Group Company: LeadSquared
Designation: Senior Talent Acquisition Specialist – AI, ML & Data Engineering Hiring
Office Location: [ Bengaluru] (4 days work from office, 1 day flexible/remote)
Position description: The Senior TA Specialist – AI, ML & Data Engineering at LeadSquared will own end-to-end recruitment across AI Engineering, Machine Learning, Data Engineering, and Data Science functions — from individual contributors to team leads. This role requires strong technical fluency across the AI/data stack, the ability to independently assess technical talent, and proven experience building AI/data teams from the ground up within a SaaS/product environment.
Primary Responsibilities
- Manage full-cycle recruitment across AI/ML and Data roles, including AI Engineers, ML Engineers, Data Engineers, Data Scientists, MLOps Engineers, Applied/Research Scientists, and AI Product roles
- Partner with hiring managers and engineering leaders to define role requirements, sourcing strategy, and hiring timelines for each specialization
- Build and execute sourcing strategies for niche AI/data talent using LinkedIn, GitHub, Kaggle, Stack Overflow, AI/data communities and conferences, and referral networks
- Screen candidates for technical fit, cultural alignment, and career motivation, factoring in the distinct skill sets of AI/ML vs. data engineering roles
- Design and continuously improve technical interview processes in collaboration with engineering stakeholders across AI, ML, and Data Engineering teams
- Manage offer negotiations and closing for competitive AI/ML/Data talent
- Build and maintain strong talent pipelines across AI, ML, and Data Engineering to support LeadSquared's growing AI initiatives
- Track and report hiring metrics (time-to-fill, source effectiveness, offer-to-join ratio) by role category
Additional Responsibilities
- Support employer branding initiatives targeted at AI/ML and Data Engineering talent communities (tech talks, hackathons, university/SaaS ecosystem partnerships)
- Advise leadership on market compensation trends and talent availability across AI, ML, and Data Engineering functions
- Mentor junior recruiters on technical sourcing and evaluation techniques for data-heavy roles
- Contribute to workforce planning for scaling LeadSquared's AI and data teams
Reporting Team
- Reporting Designation: TA Manager / Head of Talent Acquisition
- Reporting Department: Human Resources / People & Talent
Educational Qualifications Preferred
- Category: Full-time
- Field specialization: Human Resources, Business Administration, or related field (Computer Science/Engineering background is a plus)
- Degree: Bachelor's degree (master's preferred but not mandatory)
Required Work Experience
- Industry: SaaS / Technology / Product-based companies (mandatory SaaS background)
- Role: Technical Recruiter / Talent Acquisition Specialist
- Years of experience: 4–5 years overall in technical recruitment within a SaaS company, including at least 1.5 years specifically building AI/ML/Data Engineering teams (from scratch or scaling existing teams)
Key Performance Indicators
- Time-to-fill for AI/ML and Data Engineering roles
- Offer acceptance rate
- Quality of hire (retention at 6/12 months)
- Diversity of candidate pipeline
- Hiring manager satisfaction score
- Sourcing channel effectiveness across role categories
Required Competencies
- Strong stakeholder management and consultative hiring approach
- Ability to evaluate technical AI/ML and Data Engineering talent independent of engineering support
- Negotiation and closing skills for competitive/niche talent
- Data-driven decision-making
- Adaptability across multiple concurrent hiring lines (AI, ML, Data Engineering)
Required Knowledge
- Strong understanding of AI/ML and Data Engineering concepts, roles, and career paths (e.g., differences between AI Engineer, ML Engineer, Data Engineer, Data Scientist, MLOps Engineer)
- Familiarity with relevant tech stacks — Python, TensorFlow, PyTorch, LLMs, NLP, Computer Vision for AI/ML; SQL, Spark, Airflow, Kafka, ETL/ELT pipelines, cloud data platforms (AWS/GCP/Azure) for Data Engineering — enough to evaluate resumes and hold informed conversations with candidates
- Understanding of the SaaS business model and how AI/data teams are structured within product companies
- Knowledge of current compensation benchmarks and market trends across AI, ML, and Data Engineering talent
- Understanding of applicant tracking systems (ATS) and sourcing tools
Required Skills
- Advanced sourcing (Boolean search, GitHub/Kaggle mining, LinkedIn Recruiter)
- Technical screening and competency-based interviewing across multiple technical disciplines
- Strong written and verbal communication
- Pipeline and stakeholder reporting
- Employer branding and candidate experience management
Required Abilities
- Physical: Standard office/desk-based work; comfortable working from office 4 days a week
- Other: Ability to manage multiple concurrent open roles across different technical specializations; resilience in a competitive hiring market
Work Environment Details: Fast-paced, target-driven SaaS environment; 4 days work from office, 1 day flexible/remote; close collaboration with engineering, data, and leadership teams
