*Location:** Mumbai (with potential travel to multiple global office locations including the USA, UAE, Saudi Arabia, and Singapore)
*Experience Level:** 10\- 12 years (6–8 years in engineering/data science roles and at least 2\-3\+ years in a dedicated AI/ML architecture role)
*About the Role**
We are seeking a highly experienced Enterprise Data and AI Architect to design, implement, and scale enterprise\-grade AI solutions across industries such as BFSI, Retail, Manufacturing, and Healthcare. This is a strategic, hands\-on leadership role designed to transform legacy data silos into modern Data Mesh or Data Lakehouse architectures. The ideal candidate will bridge the gap between "Proof of Concept" (PoC) and full\-scale enterprise production by deploying cutting\-edge Machine Learning, Generative AI, and Agentic AI systems. You will collaborate closely with product teams, pre\-sales, and executive stakeholders to translate complex business problems into secure, scalable, and autonomous AI solutions.
Writing clean and maintainable code, Agile methodologies, Git version control, and testing methodologies
*Machine Learning Model Development**
Design, develop, deploy, and optimize advanced ML algorithms, leveraging techniques in supervised/unsupervised learning, deep learning, reinforcement learning, NLP, and computer vision.
Select appropriate mathematical and statistical modeling techniques, handling comprehensive data preparation, wrangling, feature engineering, and dimensionality reduction.
Train and deploy ML models utilizing distributed computing frameworks and tools like PyTorch, TensorFlow, MLflow, SageMaker, Azure ML, or Kubeflow.
Overhaul legacy data silos into modern Data Lakehouse or Data Mesh architectures to support predictive analytics and real\-time business intelligence.
Partner with data architects to define robust data pipelines, data governance, feature stores, and complex ETL/ELT processes.
Design secure and performant integration patterns to connect AI agents and models dynamically with ERP, CRM, workflow engines, APIs, and microservices.
*MLOps, LLMOps \& Production Operations**
Define and implement comprehensive MLOps/LLMOps standards, including CI/CD pipelines, model versioning, lifecycle observability, and rollback processes.
Establish rigorous monitoring frameworks spanning model drift detection, performance tracking, telemetry, and establishing KPIs for production AI systems.
Drive continuous improvement of model performance, computational efficiency, and operational cost.
*AI Governance, Security \& Ethics**
Establish enterprise AI governance standards, ensuring compliance with data privacy policies (PII handling), role\-based access, and IT security protocols (e.g., AWS IAM, GuardDuty).
Implement "Responsible AI" guardrails tailored for regulated industries, including fairness checks, bias detection, and safety filters.
Design human\-in\-the\-loop mechanisms for agent oversight, define autonomy thresholds, and establish escalation protocols.
Ensure complete traceability, automation audit trails, and explainability in AI\-driven decisions.
Partner with business, product, engineering, and compliance teams to translate complex business problems into viable AI/ML approaches.
Create and deliver high\-level technical presentations to executive stakeholders using data visualization tools to communicate complex strategies and business impact.
Provide technical guidance, architectural oversight, and mentorship to junior engineers.
*Required Skills \& Qualifications**
*Technical Core \& Programming:**
**Mandatory:** Strong programming proficiency in **Python \& Libraries such as Pandas, NumPy, and Scikit\-learn**
Solid **mathematical \& Statistical** foundation covering linear algebra, calculus, probability theory, and statistics.
**Database:** SQL for relational databases and NoSQL for unstructured data handling
Developing scalable microservices via RESTful APIs, FastAPI, gRPC Server
*Machine Learning \& Deep Learning:**
Proficiency with **ML frameworks** such as TensorFlow, PyTorch, and Scikit\-learn.
Hands\-on experience **implementing algorithms** like decision trees, random forests, SVMs, and neural networks.
Expertise in **deep learning architectures** (CNN, RNN, GAN).
Expertise in Natural Language Processing (NLP) and recommendation systems
Hands\-on experience with Multimodal NLP implementation, such as Computer Vision (OpenCV) and Speech\-to\-Text
Extensive experience interacting/integration with Foundation LLM Models ( such as GPT, Claude, Gemini, LLaMA, Mistral, and DeepSeek)
Proven ability in Model Fine\-Tuning \& Customization
Experience with LLM frameworks like LangChain and LlamaIndex.
Proven expertise in designing multi\-agent orchestration using LangGraph, CrewAI, AutoGen, or equivalent tools.
Hands\-on experience with AI foundries and Copilots: OpenAI, Azure AI Foundry, MSFT Copilot Studio Agent Builder, AWS Bedrock, Vertex AI, Anthropic Claude, and low\-code/no\-code platforms