Engineering Matching Specialist (focus in GenAI and AI/ML roles)

Responsibilities:

Technical Talent Advisory & Evaluation

• Perform in-depth analysis of engineering resumes and portfolios with a strong lens on software architectures, tech stacks, and system design approaches. Evaluate candidates’ real-world coding experience, scalability considerations, and project relevance against client requirements.

Engineering Capability Assessments

• Lead structured technical screenings and scenario-based interviews to probe problem-solving skills, depth of systems knowledge, and ability to apply engineering principles. Assess strengths across SDLC stages, from architecture through deployment and operations.

Strategic Client Advisory

• Advise CTOs, VPs of Engineering, and Product leaders on engineering talent strategy. Translate project requirements and product roadmaps into talent specifications, guiding clients on the types of engineers and skillsets required to scale effectively. Operate as a strategic engineering partner.

Technical Talent Pipeline Development

• Curate and maintain a network of high-caliber engineers, staying ahead of industry shifts in cloud infrastructure, distributed systems, AI/ML, DevOps, and modern frameworks. Proactively surface candidates who align with emerging client needs.

Stakeholder & Team Alignment

• Collaborate with engineering leadership to ensure candidates align not only on technical ability but also on team practices, delivery models, and long-term architectural direction.

Engineering Career Guidance

• Build trusted relationships with engineers, providing technical career guidance and mapping their expertise to market demand. Position yourself as a long-term advisor helping them navigate opportunities in modern engineering landscapes.

Continuous Technical Depth

• ​Remain current with advancements in cloud platforms, container orchestration, data engineering, AI/ML stacks, and secure development practices—translating this knowledge into sharper evaluation and client advisory.

Qualifications:

• Degree or equivalent qualification in an IT-related field, such as Computer Science, Information Technology, or Software Engineering.
• Proven experience working with Sales Teams or in a Sales/pre-sales-facing role, demonstrating an understanding of the sales process and the ability to work collaboratively with sales professionals.
• Data Engineering/Science/Generative AI Transition: A demonstrated track record of transitioning from software engineering to data engineering or data science. This could be evidenced by relevant work experience, personal projects, or certifications.
• Data Expertise: At least 2 years of experience in data-related roles, with a strong understanding of at least one of the following:
• Data Engineering: Building and maintaining data pipelines, ETL/ELT processes, and working with data warehousing solutions (e.g., Snowflake, BigQuery, Redshift).
• Generative AI (GenAI): Experience with large language models (LLMs), prompt engineering, and building applications using GenAI technologies.
• AI/ML: A solid understanding of Machine Learning concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Matching Acumen: The ability to accurately assess the technical skills and experience of candidates for Data Engineering, GenAI, and AI/ML roles. This includes understanding the nuances of different data-related technologies and how they apply to specific client needs.