Senior AI/ML Engineer

Position: Senior AI/ML Engineer

Experience: 5–8 Years

Location: Delhi

Work Mode: Hybrid – 2 Days WFO

Working Days: 5 Days a Week

Shift Timings: 9:00 AM – 6:00 PM

Notice Period: Immediate to 30 Days Preferred

Interview Process: 3 Rounds | Final Round – Face-to-Face

Key Responsibilities

Data Foundation & Instrumentation

• Design and own clickstream and impression-logging pipelines.
• Define event taxonomies that support downstream ML models.
• Normalize and match products across gift cards, merchandise, and services.
• Build product taxonomies and deduplication frameworks to support co-occurrence analysis.

Search & Retrieval

• Own search quality, including zero-result rate, query understanding, synonym/typo handling, and relevance ranking.
• Build semantic search and eligibility Q&A systems using catalogue and plan-rule data.
• Implement retrieval and generation systems with citations and appropriate abstention when information is uncertain.

Recommendations & Personalization

• Build co-purchase and cohort-based recommendations and collections.
• Develop session-based recommendations and in-session intent models.
• Build personalized collections using browsing and redemption behavior.
• Develop learned ranking models using user, item, cohort, and contextual features.
• Ensure eligibility, balance, and availability are enforced as hard constraints.
• Build utilization intelligence to identify users at risk of forfeiting funds and determine relevant engagement opportunities.

Responsible & Privacy-Aware ML

• Maintain strong architectural boundaries around health-adjacent interaction data.
• Ensure sensitive data is appropriately segregated at the schema level.
• Design systems to minimize confidently incorrect eligibility answers and enable models to abstain when they do not have sufficient information.

Serving & Reliability

• Deploy, serve, and operate ML models in production.
• Own model latency, availability, monitoring, and reliability.

Experimentation & Business Impact

• Build robust offline evaluation frameworks that reflect real-world outcomes.
• Conduct A/B testing against business metrics such as utilization rate, order frequency, and search success.
• Measure and communicate the business impact of ML initiatives.

Must-Have Skills

• 5+ years of experience in production ML or ML-adjacent engineering.
• Strong expertise in either Search & Ranking or Recommendations & Personalization, with practical understanding of the other.
• Proven experience building and shipping a production ML system from the ground up.
• Strong expertise in embeddings, vector search, hybrid lexical + semantic retrieval, and reranking.
• Experience with vector search technologies such as FAISS, HNSW, pgvector, or equivalent managed platforms.
• Experience with learned ranking, including GBDT rankers and neural/sequential approaches.
• Expert-level Python and strong SQL skills.
• Experience with Spark or equivalent data-processing frameworks.
• Experience with Airflow, Dagster, or equivalent workflow orchestration tools.
• Hands-on experience deploying and operating ML systems in production.
• Strong understanding of model serving, monitoring, reliability, and scalability.
• Rigorous approach to ML evaluation with the ability to connect technical improvements to measurable business outcomes.
• Strong ownership and self-direction, with the ability to define roadmaps, manage stakeholders, negotiate scope, and deliver independently.