AI/ML Architect

About the Role

We are looking for a senior Enterprise Data and AI Architect who can design, roll out, and grow enterprise-level AI solutions across sectors like BFSI, retail, manufacturing, and healthcare. This is a strategic, hands-on leadership position focused on converting legacy data silos into modern data mesh or data lakehouse environments. The selected professional will help move solutions from proof-of-concept stage into secure, scalable, production-ready systems using machine learning, generative AI, and agentic AI. The role requires close collaboration with product, pre-sales, and leadership teams to turn complex business needs into practical autonomous AI solutions.

Location and Travel

The role is based in Mumbai, with possible travel to multiple international office locations, including the USA, UAE, Saudi Arabia, and Singapore.

Experience Level

This role requires 10 to 12 years of overall experience, including 6 to 8 years in engineering or data science functions and at least 2 to 3+ years in a dedicated AI/ML architecture position.

Key Responsibilities

• Shape and continuously improve the enterprise AI architecture so it supports business goals, data priorities, and technology direction.
• Turn complex business problems into scalable technical AI solutions.
• Build end-to-end AI and ML architecture covering ingestion, data preparation, training, deployment, and monitoring.
• Create secure, compliant AI deployment models for cloud and hybrid environments, along with intelligent automation use cases.
• Develop reusable reference patterns for generative AI, agentic AI, predictive modeling, conversational systems, and automation.
• Assess and choose the right AI platforms, frameworks, cloud offerings, LLM orchestration tools, vector stores, and supporting technologies.
• Design and implement GenAI and LLM-based applications using retrieval-augmented generation, fine-tuning, and prompt design.
• Build agentic AI workflows with autonomous decision-making, multi-step execution, and function-calling in production settings.
• Create orchestration layers for multi-agent cooperation, memory handling, planning, evaluation, and tool use.
• Work with major AI platforms and APIs such as Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic Claude SDK, and OpenAI APIs.
• Design retrieval pipelines with vector databases and tune embeddings to improve search relevance and answer quality.
• Improve prompts, add validation controls, and reduce hallucination risk in generated outputs.
• Write clean, maintainable code and work with Agile practices, Git-based version control, and testing methods.
• Design, train, deploy, and optimize ML models across supervised, unsupervised, deep learning, reinforcement learning, NLP, and computer vision use cases.
• Apply statistical and mathematical methods while managing data preparation, wrangling, feature engineering, and dimensionality reduction.
• Use distributed training and deployment tools such as PyTorch, TensorFlow, MLflow, SageMaker, Azure ML, or Kubeflow.
• Modernize legacy data silos into data lakehouse or data mesh architectures that support analytics and real-time intelligence.
• Work with data architects to define pipelines, governance structures, feature stores, and complex ETL/ELT workflows.
• Build secure integration patterns that connect AI systems with ERP, CRM, workflow tools, APIs, and microservices.
• Set up MLOps and LLMOps standards covering CI/CD, version control, lifecycle monitoring, and rollback processes.
• Build monitoring systems for drift detection, performance measurement, telemetry, and production AI KPIs.
• Track agent performance using metrics such as task success rate, reasoning checks, error handling, and decision audit logs.
• Improve model quality, compute efficiency, and operating cost over time.
• Define enterprise AI governance rules covering privacy, PII handling, role-based access, and security controls such as AWS IAM and GuardDuty.
• Implement responsible AI safeguards for regulated environments, including fairness, bias checks, and safety filters.
• Design human-in-the-loop review points, autonomy limits, and escalation paths for AI agents.
• Ensure traceability, automation audit trails, and explainability for AI-driven decisions.
• Collaborate with business, product, engineering, and compliance stakeholders to map business problems to suitable AI/ML approaches.
• Present technical strategies and business impact to executive audiences using data visualization and clear storytelling.
• Guide and mentor junior engineers while providing architectural direction and technical oversight.

Required Skills and Qualifications

• Strong hands-on programming ability in Python, with practical use of Pandas, NumPy, and Scikit-learn.
• Solid grounding in linear algebra, calculus, probability, and statistics.
• Working knowledge of SQL for relational data and NoSQL for unstructured data.
• Ability to build scalable microservices with REST APIs, FastAPI, and gRPC.
• Experience with TensorFlow, PyTorch, and Scikit-learn.
• Practical exposure to decision trees, random forests, SVMs, and neural networks.
• Deep learning experience with CNNs, RNNs, and GANs.
• Strong understanding of NLP, recommendation systems, computer vision, and speech-to-text.
• Experience integrating with foundation models such as GPT, Claude, Gemini, LLaMA, Mistral, and DeepSeek.
• Ability to fine-tune and customize models for specific business needs.
• Hands-on use of LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar orchestration tools.
• Experience with OpenAI, Azure AI Foundry, Microsoft Copilot Studio Agent Builder, AWS Bedrock, Vertex AI, Anthropic Claude, and low-code/no-code AI tools.
• Strong data warehousing knowledge.
• Deep familiarity with the AWS ecosystem, including S3, SageMaker, Redshift, Glue, Bedrock, and EKS, with exposure to Azure or GCP.
• Experience with Snowflake, Databricks, or Apache Spark.
• Working knowledge of vector databases such as Pinecone, FAISS, Chroma, or Azure AI Search.
• Moderate experience with Terraform or AWS CloudFormation.
• Understanding of microservices, event-driven design, API-first architecture, and SQL/NoSQL data models.
• Experience across the ML/LLM lifecycle using MLflow, Kubernetes, and observability tools.
• Ability to use data visualization tools to interpret and present model insights.

Education Requirements

A bachelor’s or master’s degree in Computer Science, AI, Data Science, Engineering, or a related technical field is required.

Additional Experience Requirements

Candidates should have at least 4 years of experience in application development, engineering, or solution delivery roles; 3 years of hands-on experience in AI/ML engineering, data science, or AI solution architecture; and 2 years of experience designing and implementing scaled Agentic AI and Generative AI platforms in live operations.

Notes

This is a full-time onsite role. No stipend or salary details were provided.