Technology Titan ( Enterprise Architect- AI/ML)

careers-bmwtechworks
Department Icon Data Science Analytics & Machine Learning
Posted: 6 hours ago
8-15 years
Chennai, Tamil Nadu, India
Data Science Analytics & Machine Learning
work from office
Full Time
Closes 11 Jan 2027
Posted: 6 hours ago
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Job Description

Role

Technology Titan | Hands-on AI Enterprise Architect

Location

Chennai | Flexibility for business travel

Experience

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

What awaits you / Job Profile?

  • 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.

What should you bring along?

  • 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.
  • Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering or a related discipline, or equivalent professional experience.

Must-have technical skills

  • 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.

Good-to-have technical skills

  • 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.

Skills

PythonML FrameworksPytorchAI ToolsMLAIAPI DesignDeep LearningSecurityMachine LearningTensorFlowPyTorchScikit-learnLLM

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About Company

BMW TechWorks India is a joint venture and a 100% digital hub of the BMW Group, focusing on software development, digital transformation, and IT solutions for the global automotive giant.

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