Director - AI Engineering (AI Lab)

Title: Director – AI Engineering    

Location: Gurugram

Summary:   

We are seeking a dynamic and technically strong Director of AI Engineering to lead the e nd-to-end engineering, deployment, and lifecycle management of production-grade AI solutions. The ideal candidate will possess d eep 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   

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

2. Strategic Technology Oversight   

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

3. Cross-Functional Collaboration   

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

4. Team Leadership & Scaling   

1. Lead a team of senior engineers, AI developers, and data engineers across GDCs and member firms.
2. Mentor staff on technical excellence, agile practices, and delivery accountability.
3. 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:   

1. Master’s or Ph.D. in Computer Science, Engineering, or related field.
2. 14+ years in technology delivery, with 8+ years in AI/ML productization.
3. Proven experience delivering AI platforms and digital products at scale.
4. Strong leadership in managing distributed engineering teams and AI squads.
5. Ability to present and influence at the CXO and Board level on technology vision and delivery health.  

  KPIs and Success Metrics:   

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