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Director - AI Technology Delivery

Director - AI Technology Delivery

T D NewtonBangalore
30+ days ago
Job description

Urgent requirement for Director-AI Technology Delivery for Bangalore Location.

Experience : 15-20 Yrs

Relevant : AI- Min 8 Yrs

Job Description :

We are seeking a dynamic and technically strong Director of AI Technology Delivery to lead the end-to-end engineering, deployment, and lifecycle 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 frontend (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 :

  • Master's 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
  • ref : hirist.tech)

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