Roles & Responsibilities :

1. AI/ML Architecture :

- Design end-to-end AI/ML architectures covering data ingestion, model development, training, inference, deployment, monitoring, and governance.

- Define scalable, reliable, secure, and cost-effective architecture patterns for enterprise AI applications.

- Develop reusable AI/ML reference architectures, frameworks, design patterns, and technical standards.

- Evaluate and select appropriate ML models, algorithms, frameworks, infrastructure, and deployment approaches.

2. Generative AI & LLM Architecture :

- Architect and implement enterprise-grade GenAI/LLM solutions, including RAG, hybrid RAG, AI copilots, intelligent assistants, and agentic AI systems.

- Design solutions involving LLMs, embeddings, vector databases, prompt engineering, model evaluation, fine-tuning, and retrieval systems.

- Evaluate LLMs and determine appropriate approaches such as prompting, RAG, fine-tuning, or model customization.

- Design multi-agent workflows and orchestration architectures where applicable.

3. MLOps / LLMOps :

- Establish robust MLOps and LLMOps practices for model/application lifecycle management.

- Design CI/CD and automation pipelines for model training, validation, deployment, and monitoring.

- Implement model versioning, experiment tracking, model registry, evaluation, observability, and rollback mechanisms.

- Define monitoring strategies for model performance, data drift, hallucination, latency, reliability, and cost.

4. Security, Governance & Responsible AI :

- Design secure AI architectures covering data privacy, access control, authentication, authorization, and enterprise security.

- Establish appropriate AI governance, responsible AI, model risk, compliance, and data protection practices.

- Define guardrails and evaluation mechanisms for GenAI applications.

- Ensure AI solutions meet organizational security and regulatory requirements.

5. Technical Leadership :

- Lead architecture discussions, technical design reviews, POCs, architecture reviews, and production-readiness assessments.

- Provide technical direction and mentorship to ML engineers, data scientists, software engineers, and DevOps/MLOps teams.

- Review technical designs and ensure adherence to architecture and engineering standards.

- Act as a technical escalation point for complex AI/ML problems.

Preferred Candidate Profile :

- 5 - 10 years of overall experience in software engineering, data science, machine learning, AI engineering, or solution architecture.

- At least 3+ years of hands-on experience building and deploying AI/ML solutions.

- Proven experience designing and implementing production-grade AI/ML systems.

- Experience leading technical design or architecture for medium-to-large-scale AI/ML projects.

- Experience working with cross-functional engineering, data, product, and business teams.

- Strong programming experience in Python.

- Strong understanding of Machine Learning, Deep Learning, NLP, and Generative AI.

- Hands-on experience with frameworks such as PyTorch, TensorFlow, Scikit-learn, or equivalent.

- Strong understanding of LLMs, RAG, embeddings, vector databases, prompt engineering, and LLM evaluation.

- Experience designing production AI/ML pipelines and deploying models/services.

- Strong understanding of MLOps / LLMOps.

- Experience with at least one major cloud platform :

1. AWS

2. Microsoft Azure

3. Google Cloud Platform

- Strong understanding of APIs, microservices, containers, and distributed systems.

- Experience with Docker and Kubernetes is preferred.