Senior AI Architect

Key Responsibilities:
Strategic AI Architecture
Design, architect, and oversee implementation of end-to-end AI systems, spanning data
ingestion, model development, evaluation, deployment, and observability.
Lead architecture for agentic systems with memory, planning, and tool-use capabilities.
Build hybrid AI architectures that integrate:
o Traditional ML (XGBoost, SVM, Random Forest)
o Deep Learning (CNNs, RNNs, Transformers)
o Generative AI (LLMs, Diffusion Models, Multimodal AI)
Generative AI & Agentic Systems
Develop applications using LLMs (GPT-4/Claude/Gemini, etc.) with frameworks like:
o LangChain, LlamaIndex, Haystack
o Vector DBs (Weaviate, Pinecone, FAISS, Qdrant)
Architect RAG pipelines, prompt engineering workflows, and tool-using
agents (AutoGPT-style).
Optimize inference, memory management, and token budgeting for agent runtimes.
Traditional AI/ML & Data Science
Guide the development of supervised and unsupervised ML models for classification,
regression, clustering, forecasting, and anomaly detection.
Translate business problems into mathematical formulations and data science models.
Collaborate with Data Engineering to optimize pipelines, feature stores, and model-serving
infrastructure.
Infrastructure & Tooling
Deploy models via cloud-native platforms (AWS Sagemaker, Azure ML, Google Cloud Platform Vertex AI).
Use MLOps tools for versioning, CI/CD, drift detection (MLflow, Kubeflow, Arize).
Leverage orchestration tools like Airflow or Prefect to manage complex workflows.
Leadership & Governance
Mentor junior data scientists and AI engineers across the SDLC.
Participate in executive-level planning for AI adoption and roadmap.
Define and enforce responsible AI practices: model fairness, privacy, explainability.
Qualifications:
Required:
Bachelor's or Master s in Computer Science, AI, Data Science, or related field.
8+ years experience in AI/ML, with 3+ years in architecture or leadership roles.
Proven delivery of AI systems in production: GenAI + traditional ML.
Strong knowledge of LLMs, Transformer models, Vector embeddings, and Agents.
Experience with Python (TensorFlow, PyTorch, HuggingFace, Scikit-learn), SQL, and cloud
platforms.
Preferred:
PhD in AI, NLP, or Applied ML.
Experience integrating AI into enterprise platforms, decision support tools, or clinical
systems.
Familiarity with HIPAA, GDPR, or healthcare-specific data compliance (for regulated
environments).
Soft Skills & Traits:
Strategic thinker with strong communication skills.
Natural collaborator who can lead across data, engineering, product, and executive teams.
Proactive, detail-oriented, and passionate about emerging AI technologies.
Key Competencies
Communicates effectively Attentively listens to others, provides timely and helpful information
and is effective in a range of professional settings. Gives and receives feedback in a productive,
professional manner. Demonstrates excellent oral and written communication skills.
Manages Ambiguity -Operating effectively, even when things are not certain, or the way forward is
not clear. Is flexible in approach and is able to adapt their approach to meet changing business
needs.
Manages complexity -Makes sense of complex, high quantity, and sometimes contradictory
information to effectively solve problems. Has strong organizational skills and is able to manage
multiple activities at once. Has high attention to detail.
Ensures Accountability -Follows through on commitments and makes sure others do the same.
Able to work independently as part of a small team.