Artificial Intelligence - Technical Lead

Job Description

. Role Overview As a Technical Lead within the Digital team, you will partner with clients to navigate fast-evolving business environments and accelerate their digital transformation journey.

. You will leverage your deep technical expertise to architect innovative, scalable solutions across web, mobile, cloud, and emerging technologies.

. This is a hands-on leadership role ideal for someone passionate about Gen AI, Agentic AI, and modern engineering practices.

Key Responsibilities

. Translate business goals into Gen AI and Agentic AI enabled architectures (e.g., RAG, autonomous agents).

. Own the full solution design lifecycle across application, data, integration, security, and observability layers.

. Lead technical discussions with clients, guide delivery teams, and provide strong solution governance.

. Drive hands-on R&D initiatives, prototypes, and POCs that evolve into successful MVPs. Establish robust LLMOps capabilities including evaluation frameworks, observability, guardrails, and prompt/version management aligned with SLOs.

. Architect secure and scalable data flows (batch/stream) integrate vector search and define APIs/events for tools and agents.

. Evaluate platform and model options (managed vs OSS), optimizing for cost, performance, and risk.

. Ensure security and privacy-by-design compliance: Zero Trust, IAM/OIDC, secrets management, KMS, and data governance.

. Oversee delivery roadmaps, backlogs, multidisciplinary teams, design/code reviews, and technical standards.

. Engage stakeholders through discovery workshops, roadmap planning, estimates, and business value articulation.

. Mentor and upskill engineering teams create reusable frameworks, blueprints, and reference architectures.

Job Requirements

. Basic Qualifications Bachelor's degree or equivalent professional qualification.

. 12+ years of enterprise architecture experience with 2+ years designing production-grade Gen AI solutions.

. Strong foundation in Data Science / Machine Learning concepts.

. Proven expertise across cloud ecosystems (AWS, Azure, or GCP), containers/Kubernetes/serverless, and IaC (Terraform/CloudFormation).

. Hands-on experience with at least one RAG/Agent stack: LangChain / LangGraph, DSPy, OpenAI or Anthropic tools, Databricks Agents, etc.

. Practical LLMOps experience with evaluation: LLM judges, task metrics

. Observability: MLflow, OpenTelemetry Prompt/Version registries, CI/CD for AI workloads Strong Data & Lakehouse expertise (Delta/Iceberg/Hudi), event streaming (Kafka/Kinesis/Pub/Sub), and vector search (Pinecone, pgvector, Milvus, Vespa, etc.).

. API engineering: REST, gRPC, GraphQL, event-driven patterns and version management.

. Security standards: OAuth2/OIDC, JWT/mTLS, secrets governance, classification/lineage, safety guardrails.

. Excellent communication skills with ability to articulate ROI, risks, and architecture decisions to technical and non-technical stakeholders.

. Proven experience in managing multiple initiatives and client expectations effectively.

. Strong leadership mindset with continuous learning and delivery excellence focus.

Preferred Certifications

. AWS/Azure/Google AI Certifications AWS/Azure/Google Data Science Certifications AWS/Azure/Google Solution Architect Certifications TOGAF 9 Other relevant industry-standard architecture certifications