Neurosymbolic Marketing Intelligence Engineer

You will design and deploy neurosymbolic AI systems that combine the pattern-recognition power of neural networks with the explainability and rule-based precision of symbolic reasoning — turning raw marketing data into actionable, interpretable intelligence. Your work will directly influence campaign strategy, customer segmentation, and real-time decisioning across enterprise clients.

• Architect and implement neurosymbolic models that integrate neural representations with symbolic knowledge graphs for marketing attribution and audience understanding.
• Develop NLP pipelines and LLM integration layers to extract structured insights from unstructured marketing content — emails, surveys, social signals, and campaign copy.
• Build and maintain marketing analytics dashboards and real-time intelligence APIs serving segmentation, propensity scoring, and next-best-action recommendations.
• Collaborate with product and growth teams to embed AI-driven marketing intelligence into client platforms, ensuring explainability and compliance with data-privacy regulations.
• Design evaluation frameworks using controlled experiments, counterfactual analysis, and causal inference to measure model impact on marketing KPIs.
• Mentor junior engineers and contribute to internal research on hybrid AI approaches for vertical-specific marketing use cases.

• Fundamentals of neurosymbolic AI: understand how deep learning and symbolic logic can be unified — rules, ontologies, and neural embeddings working in concert.
• Core statistical and causal inference techniques: causal graphs, do-calculus, and counterfactual reasoning as applied to marketing mix modelling and uplift estimation.
• Marketing science foundations: customer journey mapping, attribution models (first-touch, last-touch, Markov, Shapley), and lifetime value (LTV) estimation.

• Python — proficiency with PyTorch/JAX, LangChain/LlamaIndex, and graph libraries like NetworkX or PyG.
• Knowledge-graph tooling — Neo4j, RDF/SPARQL, OWL/DL reasoning, and vector-database integration (e.g., Milvus, Weaviate).
• MLOps and production deployment — MLflow, Kubeflow, Docker/Kubernetes, CI/CD for ML pipelines on AWS SageMaker or equivalent.
• Marketing analytics stacks — SQL, dbt, Looker/Metabase, and experience with CDPs (Customer Data Platforms) like Segment or mParticle.
• LLM orchestration and RAG — prompt engineering, function calling, retrieval-augmented generation, and guardrail implementation.

• A compulsive habit of reverse-engineering why a model's recommendation diverged from the ground-truth marketing outcome — pursuing interpretability over accuracy alone.
• Regularly exploring the intersection of cognitive science and AI — drawing analogies between human heuristic reasoning and symbolic rule systems.
• An instinct for turning vague business questions (e.g., 'Why are our email open rates dropping?') into testable AI hypotheses with measurable feedback loops.

• Competitive annual CTC of ₹12L–₹20L with performance-based bonuses and ESOP eligibility.
• Fully remote-first setup with a home-office stipend and annual tech-budget allowance.
• Access to dedicated GPU cloud credits (A100/H100 hours) for personal research and side projects.
• Structured mentorship from senior AI researchers and bi-weekly tech-salon sessions on emerging neurosymbolic literature.
• Sabbatical-style learning weeks twice a year — paid time off dedicated to conference attendance, certifications, or independent R&D.