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.