We are seeking an experienced, product-focused Senior AI Researcher to transform the latest breakthroughs in large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI into high-impact product capabilities. You will lead end-to-end applied research initiatives—from rapid prototyping through production deployment—while mentoring teammates and collaborating closely with product, engineering, and business stakeholders to deliver measurable business value.

Key Responsibilities

• Translate recent advances in LLMs, RAG pipelines, and agentic frameworks into production-ready solutions that solve real business problems.
• Design, prototype, and validate AI/ML models; run controlled experiments; and iterate quickly toward deployable systems.
• Own the product lifecycle for AI initiatives: define requirements, scope MVPs, set timelines, and align deliverables with strategic objectives.
• Guide and mentor junior data scientists and engineers in experimental design, model selection, and rigorous evaluation.
• Collaborate with data engineering to build and maintain robust real-time and batch data pipelines that power AI workloads.
• Establish best practices for model evaluation, testing, monitoring, and MLOps (CI/CD, automated retraining, observability).
• Conduct structured literature reviews and competitive analyses to keep the team current on emerging techniques and tools.
• Communicate technical concepts and research findings to both technical and non-technical audiences, converting insights into actionable product roadmaps.
• Maintain high coding standards through documentation, code reviews, and knowledge sharing sessions.

Minimum Qualifications

• Bachelor’s degree in Computer Science or a related field is required
• Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field is preferred
• 5+ years of post-graduate, industry or applied-research experience developing and deploying AI/ML solutions.
• Proven track record of shipping products or features that leverage LLMs, generative AI, or other large-scale machine-learning models.
• Expert-level Python programming skills and hands-on experience with modern ML frameworks.
• Practical experience with cloud environments (AWS, GCP, or Azure) and big-data technologies (e.g., Spark, Elasticsearch, vector databases).
• Solid foundations in algorithms, data structures, optimization, and statistical learning.
• Exceptional written and verbal communication skills, with the ability to influence stakeholders across disciplines.

Preferred Qualifications

• Familiarity with MLOps tooling (MLflow, Airflow, SageMaker) and best-in-class CI/CD pipelines.
• Experience leading cross-functional teams or initiatives that brought research prototypes to production at scale.
• Contributions to open-source projects, peer-reviewed publications, or patents in applied machine learning or NLP.
• Domain expertise in reinforcement learning-from-human-feedback (RLHF), prompt engineering, or vector search.