01Key Responsibilities
GenAI Architecture Solution Leadership
- Lead GenAI solution design: Architect enterprise LLM apps (RAG, agents, automation) from prototype to production.
- Fine-tune adapt models: Apply LoRA/QLoRA/PEFT (and alignment where applicable) for domain use cases.
- Agentic workflows: Design multi-step orchestration using frameworks such as LangGraph/AutoGen/CrewAI.
- Evaluation quality: Own LLM evaluation, hallucination mitigation, and responsible AI standards (e. g. , RAGAS/TruLens/DeepEval).
- Knowledge infrastructure: Govern vector DB/search patterns and knowledge integrations for scalable retrieval.
Advanced ML, Modeling Statistical Expertise
- Own the model lifecycle: Frame problems, define data strategy, build models, deploy, monitor, and iterate.
- Advanced modeling: Apply ML/NLP/time series/causal and related methods to ambiguous, highimpact problems.
- Governance: Lead documentation, validation, and risk controls aligned to responsible AI and regulated needs (e. g. , GxP).
- Experimentation: Design studies (A/B, quasi-experimental) to drive evidence-based decisions.
Data Strategy, Engineering Platform Collaboration
- Data strategy: Define acquisition, preprocessing, and enrichment for structured/unstructured enterprise data.
- EDA insights: Surface patterns and opportunities from multi-domain datasets.
- Pipelines: Co-design scalable data/ML pipelines with engineering/platform teams.
Technical Leadership Team Development
- Technical direction: Drive architecture, standards, and engineering best practices across the pod.
- Mentorship: Coach DS-1/DS-2 via reviews, pairing, and growth feedback.
- Reusable assets: Build/maintain shared frameworks, accelerators, and templates.
- Knowledge sharing: Lead workshops, documentation, and community-of-practice efforts.
Stakeholder Engagement Executive Communication
- Stakeholder partnership: Align senior leaders on priorities, value, and adoption
- Problem framing: Translate ambiguity into scoped workstreams with KPIs and milestones.
- Executive communication: Present architectures, results, and recommendations clearly.
- Delivery leadership: Manage priorities/risks across multiple initiatives in a matrixed environment.
Skills Competencies:
GenAI Advanced AI Expertise
- LLMs: Production experience deploying and integrating foundation models.
- RAG: Design and optimize retrieval (hybrid search, reranking, context strategies).
- Fine-tuning/alignment: Apply LoRA/QLoRA/PEFT and related techniques.
- Agents: Build orchestrated workflows using common agent frameworks.
- Responsible AI: Bias/safety controls, evaluation, and governance in regulated settings.
Core Data Science, ML Statistical Skills
- Programming: Advanced Python; familiarity with distributed tooling as needed.
- Statistics: Inference, Bayesian methods, experimentation, and causal thinking.
- ML breadth: Supervised/unsupervised methods; strong model selection and tuning skills.
- Data at scale: Strong SQL and experience working with large datasets/cloud data services.
Cloud, LLMOps Engineering Excellence
- Cloud: Deploy ML/GenAI solutions on AWS or Azure in production.
- LLMOps/MLOps: Versioning, CI/CD, monitoring, and safe rollout patterns.
- Containers: Docker/Kubernetes for scalable workloads.
- Engineering: Git/SDLC, APIs, and maintainable production code.
Leadership, Communication Strategic Thinking
- Communication: Explain complex AI tradeoffs to senior audiences.
- Strategy: Turn ambiguity into roadmaps with measurable outcomes.
- Leadership: Set quality bars and drive architectural decisions.
Experience Qualifications:
- Education: MS/PhD in a quantitative discipline (PhD preferred).
- Research/applied work: Evidence of solid ML/AI/NLP/statistical project experience.
- Experience: 5+ years delivering production DS/ML solutions end-to-end.
- GenAI depth: Hands-on LLMs, RAG, fine-tuning, and agentic workflows (core requirement).
- Technical leadership: Lead workstreams, mentor others, and make design decisions.
- Advanced ML: Build/validate/deploy complex models with measurable impact.
- Cross-functional delivery: Partner across business, engineering, and domain teams to ship.
Good to Have:
- Thought leadership: Publications, talks, or open-source in ML/GenAI.
- Multi-modal exposure: Experience with vision-language or related models.
- Knowledge graphs: Familiarity with biomedical ontologies/graphs is a plus.
- Commercial analytics: Exposure to .
