Talent Acquisition Executive (Data & AI)

Talent Acquisition Executive (Data \& AI)

Location: Jaipur / Noida

Full Time Permanent

Excellent Salary and growth Opportunity

Purpose

As a Senior Talent Executive for Data \& AI, you will lead the end-to-end recruitment lifecycle for high-impact technical roles. You will partner with engineering leaders, data science heads, and executive stakeholders to identify, attract, and hire top-tier talent in Data Science, Machine Learning (ML), Deep Learning, Generative AI, and Data Engineering.

Experience \& Qualifications

• Experience:

5\+ years of experience in technical recruitment, with at least

2–3 years dedicated exclusively to Data, Analytics, and AI hiring

.

• Education:

Bachelor’s degree in Human Resources, Business Administration, Computer Science, or a related field. (An engineering background is a massive plus for this specific niche).

• Proven Track Record:

Experience hiring for roles like

Lead Data Scientist, MLOps Engineer, NLP Engineer, Head of AI, and Principal Data Engineer

.

Key Responsibilities

• Strategic Sourcing \& Pipeline Building:

Devise advanced sourcing strategies to target passive talent in highly specialized fields (e.g., Computer Vision, NLP, LLMs, MLOps). Utilize GitHub, Kaggle, Hugging Face, AI research papers, and niche communities alongside traditional platforms like LinkedIn Recruiter.

• Stakeholder Management:

Act as a trusted advisor to data and AI leadership. Conduct intake meetings to deeply understand technical requirements, team dynamics, and project scopes.

• Technical Screening:

Conduct initial rigorous screenings. You must be able to evaluate a candidate’s conceptual understanding of data structures, cloud data warehouses, modeling frameworks, and AI ethics beyond just scanning for keywords.

• Market Intelligence:

Stay ahead of industry trends, talent movements, and compensation benchmarks within the Data \& AI landscape. Provide data-driven insights to leadership regarding talent scarcity and hiring bottlenecks.

• Candidate Experience:

Manage a seamless, transparent, and highly engaging candidate journey, ensuring a positive brand image even for unsuccessful applicants.

• Diversity \& Inclusion (D\&I):

Actively drive D\&I initiatives by ensuring diverse candidate slates for specialized technical roles.

Required Skills \& Competencies

1\. Domain Knowledge (Data \& AI)

• Tech Stack Fluency:

A strong conceptual grasp of what different roles do and the tools they use:

• Data Science/AI:

Python, R, PyTorch, TensorFlow, Scikit-Learn.

• Data Engineering:

SQL, Spark, Hadoop, Kafka, Snowflake, Databricks.

• Cloud Platforms:

AWS, GCP, Azure.

• GenAI/LLMs:

LangChain, LlamaIndex, vector databases (Pinecone, Milvus), fine-tuning concepts.

• Role Differentiation:

Ability to sharply distinguish between a Data Engineer, a Data Scientist, an ML Engineer, and an AI Researcher.

2\. Talent Acquisition Expertise

• Advanced Sourcing:

Mastery of boolean search, x-ray searching, and scouting non-traditional talent pools (tech conferences, open-source contributors).

• Closing \& Negotiation:

Proven track record of closing complex, high-comp offers against multiple competing bids.

3\. Soft Skills

• Technical Credibility:

Ability to speak the "language" of data scientists and engineers to build instant rapport.

• Agility \& Resilience:

Thriving in a fast-paced environment where AI tech stacks evolve rapidly.