Technology Titan ( Enterprise Architect- AIML)

Role

Technology Titan Hands-on AI Enterprise Architect

Chennai Flexibility for business travel

15 years in technology including 8 years in enterprise AI/ML GenAI or platform architecture

• Own the enterprise AI strategy and target architecture from business-problem discovery through production deployment adoption and measurable value realization.
• Determine when to use deterministic software predictive ML GenAI retrieval-augmented generation or agentic workflows rather than applying AI by default.
• Act as a hands-on architect by building reference implementations and critical components reviewing code and prompts diagnosing production issues and guiding teams through architecture and design decisions.
• Design the enterprise Agentic AI Platform including model gateways agent orchestration tool and API integration MCP-compatible connectivity context and memory knowledge retrieval identity policy enforcement evaluation observability and auditability.
• Lead the transition from conventional SDLC and AI-assisted SDLC to AI-DLC embedding AI agents across discovery requirements architecture engineering testing security release and operations with explicit human approval gates and autonomy boundaries.
• Establish reusable AI platform capabilities reference architectures golden paths and engineering standards across cloud hybrid and approved on-premises environments.
• Architect secure and production-grade AI solutions covering data pipelines model selection RAG fine-tuning multimodal experiences inference optimization LLMOps/MLOps resilience latency and cost controls.
• Embed Responsible AI privacy cybersecurity intellectual-property protection model risk and regulatory controls into architecture and delivery workflows by design.
• Define evaluation and business KPIs for quality groundedness task success safety latency adoption unit economics and ROI; use evidence to decide whether to scale redesign or stop an initiative.
• Present and negotiate with product owners on platform strategy and AI risks; mentor AI engineers data scientists architects and product teams.

• Proven delivery of enterprise AI solutions from strategy and use-case selection through hands-on implementation production operations and scaled adoption.
• Deep expertise in AI/ML Generative AI and agentic systems with sound judgment on model data platform and integration trade-offs.
• Strong software and platform engineering foundation across distributed systems APIs event-driven architectures cloud DevSecOps and end-to-end lifecycle management.
• Experience leading platform and transformation programs across multiple products teams and geographies while balancing innovation control reliability and cost.
• Executive-level communication storytelling facilitation and negotiation skills including expectation management with CEO COO and CTO.
• Ability to decompose ambiguous business problems quantify value establish a baseline and design experiments or pilots with clear exit criteria.
• Bachelors or Masters degree in Computer Science AI Data Science Engineering or a related discipline or equivalent professional experience.

• Programming and AI engineering: advanced Python; ML and deep-learning frameworks such as PyTorch TensorFlow or scikit-learn; API and microservice development.
• GenAI architecture: foundation-model selection prompt and context engineering structured outputs embeddings vector and hybrid search RAG reranking fine-tuning and multimodal solutions.
• Agentic AI: planning and reasoning patterns tool use multi-agent orchestration state and memory human-in-the-loop controls workflow recovery sandboxing and least-privilege execution.
• Agent frameworks and protocols: hands-on experience with relevant stacks such as Semantic Kernel LangGraph AutoGen or cloud-native agent services plus MCP-style tool integration where appropriate.
• Cloud AI platforms: strong expertise in at least one of Microsoft Azure AI/Foundry AWS Bedrock/SageMaker or Google Vertex AI including identity networking security deployment and cost architecture.
• AI platform engineering: Docker Kubernetes Infrastructure as Code CI/CD/CT GitOps model and prompt registries experiment tracking feature or data pipelines and artifact lineage.
• LLMOps/MLOps and observability: MLflow or equivalent automated evaluation tracing token and latency monitoring drift and data-quality detection canary or shadow deployment rollback and incident response.
• AI evaluation and safety: offline and online evaluation datasets task-success and groundedness measures hallucination and citation checks red-teaming prompt-injection defenses content and tool guardrails.
• Data and knowledge architecture: data lakes/lakehouses streaming with Kafka or equivalent governance metadata lineage knowledge graphs and semantic models.
• Governance and security: Responsible AI privacy by design threat modeling and familiarity with NIST AI RMF ISO/IEC 42001 OWASP Top 10 for LLM Applications and applicable data-protection requirements.

• Experience building enterprise developer platforms or AI factories that provide reusable agent templates evaluation harnesses approved model catalogues and self-service deployment paths.
• Hands-on experience with open-source and commercial models model routing quantization distillation GPU capacity planning and inference optimization.

Knowledge of advanced RAG GraphRAG knowledge graphs synthetic data reinforcement learning digital twins edge AI or autonomous systems.

Experience applying agentic approaches to software delivery including requirements architecture coding testing security remediation release and operations.

Automotive or regulated-industry experience including safety cybersecurity privacy and traceability expectations.

Credentials such as Microsoft Azure AI Engineer/Architect AWS Machine Learning or Solutions Architect Google Professional Machine Learning Engineer Databricks Kubernetes or TOGAF.

Required Experience:

Director