Senior US IT Recruiter

Job Summary

We are looking for a Senior US IT Recruiter with strong experience in US IT staffing, W2 hiring, Financial Services recruitment, and MSP/VMS accounts. The ideal candidate should have hands-on experience supporting clients such as Fannie Mae, Freddie Mac, Infosys, and Pontoon, with expertise in sourcing and closing candidates for Contract, Contract-to-Hire, and Full-Time positions.

Key Responsibilities

• Manage the end-to-end US IT recruitment lifecycle.
• Handle client requirements across Fannie Mae, Freddie Mac, Infosys, Pontoon, and other MSP/VMS accounts.
• Source candidates through LinkedIn Recruiter, Dice, Monster, CareerBuilder, JobDiva, ATS platforms, referrals, and networking.
• Recruit W2 consultants and build strong candidate pipelines.
• Screen candidates and assess technical skills against client requirements.
• Hire for Financial Services, Banking, Mortgage, Lending, Capital Markets, FinTech, and Insurance domains.
• Recruit for GenAI, AI/ML, LLMs, RAG, Full Stack, Java, .NET, Python, Cloud, DevOps, and Data Engineering roles.
• Ensure quality submissions within client SLAs and aggressive deadlines.
• Coordinate interviews, offer negotiations, onboarding, and joining formalities.
• Achieve monthly recruitment targets and improve submission-to-interview and interview-to-placement ratios.
• Identify and close immediate joiners and actively available consultants.

Required Skills

• 3 to 5 years of US IT Recruitment experience.
• Strong expertise in W2 hiring and US staffing.
• Hands-on experience with MSP/VMS accounts.
• Experience working with Fannie Mae, Freddie Mac, Infosys, and Pontoon.
• Proven Financial Services recruitment experience.
• Experience hiring for GenAI, AI/ML, Data Engineering, and Full Stack roles.
• Excellent communication, negotiation, sourcing, and stakeholder management skills.
• Ability to manage multiple requirements and meet tight deadlines.

Preferred Skills

• Knowledge of Agentic AI, GenAI, LLMs, RAG, and MLOps.
• Familiarity with AWS Bedrock, Azure OpenAI, LangChain, and vector databases.
• Exposure to Snowflake, Databricks, Hadoop, Spark, and Kafka.