Partner with business executives and product leaders to translate complex business objectives into quantifiable AI metrics and scientific experiments.
AI Quality, Evaluation & Trust Blueprinting
Establish enterprise-wide evaluation standards, benchmarking frameworks, and automated testbeds for hallucination detection, factual precision, bias mitigation, and toxicity prevention.
Architect continuous monitoring and statistical evaluation mechanisms for live production systems (online LLM observability, output drift, and semantic regression testing).
Lead Responsible AI, Explainability, and AI Governance initiatives—defining scientific methodologies to audit AI decisions in highly regulated enterprise environments.
Cross-Functional Collaboration & Technical Mentorship
Work in lockstep with Enterprise AI Architects and AI Engineering teams to seamlessly transition research prototypes into scalable, production-ready microservices.
Mentor and upskill senior data scientists, applied ML engineers, and offshore team members on advanced ML methods, statistical evaluation rigor, and research best practices.
Represent the enterprise as a subject matter expert in AI innovation, presenting key technical breakthroughs to executive stakeholders.
Technical Experience & Qualifications
Must-Have Experience & Skills
10–15+ years of progressive experience in Data Science, Machine Learning, and Applied Research, with 4+ years leading Generative AI & Deep Learning initiatives.
Deep theoretical and practical mastery of LLM architectures, attention mechanisms, embedding spaces, fine-tuning techniques (PEFT, LoRA, QLoRA), and model alignment (DPO, RLHF).
Proven track record designing enterprise-grade RAG architectures, vector search ecosystems (Pinecone, Qdrant, OpenSearch, pgvector), and hybrid retrieval algorithms.
Industry expertise in designing automated LLM evaluation frameworks (e.g., RAGAS, DeepEval, TruLens) and ground-truth dataset curation methods.
Deep fluency in Python and ML/DL frameworks (PyTorch, Scikit-learn, Pandas, NumPy).
Experience deploying and optimizing models across cloud platforms (Azure AI Foundry, AWS Bedrock, Databricks/MLflow).
Preferred Skills
Hands-on expertise with GraphRAG, Knowledge Graphs, and graph databases (Neo4j).
Strong background in Banking, Financial Services, or similarly regulated enterprise domains.
Experience with distributed computing frameworks (PySpark, Databricks) for large-scale data processing and model evaluation.
Publication record or open-source contributions in applied NLP, Information Retrieval, or Generative AI.
Core Competencies & Soft Skills
Scientific Rigor: Unwavering commitment to statistical validation, empirical testing, and evidence-based decision making.
Business Acumen: Ability to tie complex technical metrics (e.g., Normalized Discounted Cumulative Gain, BLEU/ROUGE, context precision) directly to ROI and business KPIs.
Thought Leadership: Clear, persuasive communication style capable of building consensus between research scientists, platform engineers, and business leaders.
Compensation, Benefits and Duration
Minimum Compensation: USD 64,000
Maximum Compensation: USD 224,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
