High Salary Director – AIML \ Generative AI Architect

Job Description

We are seeking a dynamic and technically strong Director of AI Technology Delivery to lead the end-to-end engineering, deployment, and life cycle management of production-grade AI solutions. The ideal candidate will possess deep expertise in AI/ML systems, full-stack engineering, database design, and enterprise technology frameworks, and must demonstrate proven experience in driving AI products from prototype to scaled deployment across global business units.

Key Responsibilities:

1. Technology Delivery & Execution

- Own the end-to-end delivery of AI-enabled products, from data ingestion to model deployment and integration into production environments.
- Lead full-stack development teams, including front-end (React.js, Angular), backend (Python, Node.js, Java), APIs (REST/GraphQL), and microservices architecture (Docker, Kubernetes, Kafka).
- Architect, implement, and oversee ML Ops pipelines, ensuring models are versioned, tested, monitored, and deployed securely and reliably.
- Manage high-performance databases (SQL, NoSQL, Graph, Vector DBs) for structured, semi-structured, and unstructured data across AI pipelines.

2. Strategic Technology Oversight

- Define and maintain technical blueprints, solution architecture, and delivery plans for enterprise AI solutions.
- Act as the technical custodian for AI products, aligning with cloud (Azure, AWS, GCP) infrastructure and enterprise security policies.
- Oversee development standards, CI/CD pipelines, observability, and performance tuning for all AI applications.

3. Cross-Functional Collaboration

- Partner with Product Management, AI Research, DevSecOps, UX Design, and Data Engineering teams to deliver AI-based features.
- Ensure alignment between AI experimentation and scalable productionization across business units such as Tax, Audit, ESG, and Advisory.
- Lead technical governance, code reviews, architectural reviews, and compliance against responsible AI frameworks.

4. Team Leadership & Scaling

- Lead a team of senior engineers, AI developers, and data engineers across GDCs and member firms.
- Mentor staff on technical excellence, agile practices, and delivery accountability.
- Drive talent acquisition and technical skill development across the AI delivery squads.

Core Competencies & Skills Required:

Area

Skillset

AI/ML Technologies

Hands-on with Python, PyTorch/TensorFlow, LangChain, LLMs (GPT, Claude, Falcon), Hugging Face, ONNX

Full Stack

React.js/Next.js, Node.js/Express, Django/FastAPI, GraphQL, REST APIs

DevOps/MLOps

Docker, Kubernetes, Airflow, MLflow, GitHub Actions, Azure DevOps, Ray, Terraform

Data & DBs

PostgreSQL, MongoDB, Redis, Neo4j, Milvus/Weaviate/FAISS, Azure Data Lake, Snowflake

Cloud Platforms

Azure AI Studio, AWS SageMaker, Google Vertex AI

Architecture

Event-driven, Microservices, Serverless, API Gateways, Hybrid Edge-to-Cloud Design

Security & Compliance

OAuth2, RBAC, Zero Trust, PII masking, Audit trails, Model explainability (SHAP, LIME)

Delivery Frameworks

Agile, Scrum, SAFE Agile, JIRA, Confluence, GitHub Projects

Qualifications:

- Masters or Ph.D. in Computer Science, Engineering, or related field.
- 14+ years in technology delivery, with 8+ years in AI/ML productization.
- Proven experience delivering AI platforms and digital products at scale.
- Strong leadership in managing distributed engineering teams and AI squads.
- Ability to present and influence at the CXO and Board level on technology vision and delivery health.

KPIs and Success Metrics:

- Delivery of AI products on time, within budget, and with measurable business impact.
- AI solution uptime, model performance (latency, accuracy), and scalability.
- Developer productivity (e.g., PR velocity, commit quality, MTTR).
- AI model governance compliance and audit-readiness.
- Team engagement, retention, and skill advancement across engineering pods

---